Paper Digest: CIKM 2017 Highlights

November 1, 2017June 26, 2020 admin

The ACM Conference on Information and Knowledge Management (CIKM) is an annual computer science research conference dedicated to information management and knowledge management.

To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights / summaries to quickly get the main idea of each paper.

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TABLE 1: CIKM 2017 Papers

	Title	Authors	Highlight
1	Machine Learning @ Amazon	Rajeev Rastogi	I will then talk about three specific applications where we use a variety of methods to learn semantically rich representations of data: question answering where we use deep learning techniques, product size recommendations where we use probabilistic models, and fake reviews detection where we use tensor factorization algorithms.
2	Deception Detection: When Computers Become Better than Humans	Rada Mihalcea	In this talk, I will describe our work in building linguistic and multimodal algorithms for deception detection, targeting deceptive statements, trial videos, fake news, identity deceptions, and also going after deception in multiple cultures.
3	When Deep Learning Meets Transfer Learning	Qiang Yang	In this talk, I will give an overview of how transfer learning can help alleviate these problems.
4	A Hyper-connected World	K. Ananth Krishnan	A Hyper-connected World
5	Jointly Modeling Static Visual Appearance and Temporal Pattern for Unsupervised Video Hashing	Chao Li, Yang Yang, Jiewei Cao, Zi Huang	In this paper, we propose to jointly model static visual appearance and temporal pattern for video hash code generation, as both of them are believed to be carrying important information for learning an effective hash function.
6	Construction of a National Scale ENF Map using Online Multimedia Data	Hyunsoo Kim, Youngbae Jeon, Ji Won Yoon	In this paper, we proposed a novel approach to constructing the worldwide ENF map by analyzing streaming data obtained by online multimedia services, such as "Youtube", "Earthcam", and "Ustream" instead of expensive specialized hardware.
7	Dual Learning for Cross-domain Image Captioning	Wei Zhao, Wei Xu, Min Yang, Jianbo Ye, Zhou Zhao, Yabing Feng, Yu Qiao	In this paper, we propose a cross-domain image captioning approach that uses a novel dual learning mechanism to overcome this barrier.
8	A New Approach to Compute CNNs for Extremely Large Images	Sai Wu, Mengdan Zhang, Gang Chen, Ke Chen	In this paper, we propose a new approach that adopts the BSP (bulk synchronization parallel) model to compute CNNs for images of any size.
9	Active Sampling for Large-scale Information Retrieval Evaluation	Dan Li, Evangelos Kanoulas	In this paper we seek to solve the problem of large-scale retrieval evaluation combining the two approaches.
10	Intent Based Relevance Estimation from Click Logs	Prakash Mandayam Comar, Srinivasan H. Sengamedu	In this paper, we outline a technique to model the interplay of query, user intent and position bias with respect to the relevance of the retrieved search results.
11	A Comparison of Nuggets and Clusters for Evaluating Timeline Summaries	Gaurav Baruah, Richard McCreadie, Jimmy Lin	In this paper, by building test collections that have both nugget and cluster annotations, we are able to compare these two approaches.
12	Sensitive and Scalable Online Evaluation with Theoretical Guarantees	Harrie Oosterhuis, Maarten de Rijke	Our contribution is two-fold.
13	Users Are Known by the Company They Keep: Topic Models for Viewpoint Discovery in Social Networks	Thibaut Thonet, Guillaume Cabanac, Mohand Boughanem, Karen Pinel-Sauvagnat	To address this task, we propose in this paper a novel unsupervised topic model, the Social Network Viewpoint Discovery Model (SNVDM).
14	Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network	Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang	To solve this problem, we propose a HiErarchical ATtention (HEAT) network for aspect-level sentiment classification.
15	Dyadic Memory Networks for Aspect-based Sentiment Analysis	Yi Tay, Luu Anh Tuan, Siu Cheung Hui	This paper proposes Dyadic Memory Networks (DyMemNN), a novel extension of end-to-end memory networks (memNN) for aspect-based sentiment analysis (ABSA).
16	Modeling Language Discrepancy for Cross-Lingual Sentiment Analysis	Qiang Chen, Chenliang Li, Wenjie Li	In this paper, we aim to model the language discrepancy in sentiment expressions as intrinsic bilingual polarity correlations (IBPCs) for better cross-lingual sentiment analysis.
17	Multi-view Clustering with Graph Embedding for Connectome Analysis	Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin	To solve this problem, in this paper we propose a Multi-view Clustering framework on graph instances with Graph Embedding (MCGE).
18	Attributed Signed Network Embedding	Suhang Wang, Charu Aggarwal, Jiliang Tang, Huan Liu	Therefore, in this paper, we study the novel problem of signed social network embedding with attributes.
19	Enhancing the Network Embedding Quality with Structural Similarity	Tianshu Lyu, Yuan Zhang, Yan Zhang	We present a new method, SNS, that performs network embeddings using structural information (namely graphlets) to enhance its quality.
20	On Embedding Uncertain Graphs	Jiafeng Hu, Reynold Cheng, Zhipeng Huang, Yixang Fang, Siqiang Luo	To tackle these problems, we propose a solution called URGE, or UnceRtain Graph Embedding.
21	A Large Scale Prediction Engine for App Install Clicks and Conversions	Narayan Bhamidipati, Ravi Kant, Shaunak Mishra	In this paper, we describe (a) how we built a scalable machine learning pipeline from scratch to predict the probability of users clicking and installing apps in response to ad impressions, (b) the novel features we developed to improve our model performance, (c) the training and scoring pipelines that were put into production, (d) our A/B testing process along with the metrics used to determine significant improvements, and (e) the results of our experiments.
22	Building Natural Language Interfaces to Web APIs	Yu Su, Ahmed Hassan Awadallah, Madian Khabsa, Patrick Pantel, Michael Gamon, Mark Encarnacion	We propose a novel approach to collect training data for NL2API via crowdsourcing, where crowd workers are employed to generate diversified NL commands.
23	UFeed: Refining Web Data Integration Based on User Feedback	Ahmed El-Roby, Ashraf Aboulnaga	In this paper, we introduce UFeed, a system that refines relational mediated schemas and mappings based on user feedback over query answers.
24	Extracting Records from the Web Using a Signal Processing Approach	Roberto Panerai Velloso, Carina F. Dorneles	We present here a novel approach, fully automatic and computationally efficient, using signal processing techniques to detect regularities and patterns in the structure of web pages.
25	A Scalable Graph-Coarsening Based Index for Dynamic Graph Databases	Akshay Kansal, Francesca Spezzano	In this paper, we propose a new index based on graph-coarsening to speed up subgraph query answering time in dynamic graph databases.
26	Natural Language Question/Answering: Let Users Talk With The Knowledge Graph	Weiguo Zheng, Hong Cheng, Lei Zou, Jeffrey Xu Yu, Kangfei Zhao	In this paper, we present a data + oracle approach to answer NLQs over knowledge graphs.
27	Keyword Search on RDF Graphs – A Query Graph Assembly Approach	Shuo Han, Lei Zou, Jeffery Xu Yu, Dongyan Zhao	In order to solve that, we design some heuristic lower bounds and propose a bipartite graph matching-based best-first search algorithm.
28	Region Representation Learning via Mobility Flow	Hongjian Wang, Zhenhui Li	In this paper, we are interested in learning vector representations for regions using the large-scale taxi flow data.
29	Learning Visual Features from Snapshots for Web Search	Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Liang Pang, Xueqi Cheng	In this work, we propose to learn rich visual features automatically from the layout of Web pages (i.e., Web page snapshots) for relevance ranking.
30	DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval	Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Jingfang Xu, Xueqi Cheng	In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process.
31	Learning to Un-Rank: Quantifying Search Exposure for Users in Online Communities	Asia J. Biega, Azin Ghazimatin, Hakan Ferhatosmanoglu, Krishna P. Gummadi, Gerhard Weikum	In this paper, we propose the first model for quantifying search exposure on the service provider side, casting it into a reverse k-nearest-neighbor problem.
32	Balancing Speed and Quality in Online Learning to Rank for Information Retrieval	Harrie Oosterhuis, Maarten de Rijke	Our contribution is twofold.
33	Crowd-enabled Pareto-Optimal Objects Finding Employing Multi-Pairwise-Comparison Questions	Chang Liu, Yinan Zhang, Lei Liu, Lizhen Cui, Dong Yuan, Chunyan Miao	To address this issue, we propose an algorithm, which uses preference relations given by crowdsourcing, to find Pareto-optimal objects with shorter latency and lower monetary costs.
34	Destination-aware Task Assignment in Spatial Crowdsourcing	Yan Zhao, Yang Li, Yu Wang, Han Su, Kai Zheng	In this paper we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks.
35	Crowdsourced Selection on Multi-Attribute Data	Xueping Weng, Guoliang Li, Huiqi Hu, Jianhua Feng	To address this problem, we propose predicate order based framework to reduce monetary cost.
36	Select Your Questions Wisely: For Entity Resolution With Crowd Errors	Vijaya Krishna Yalavarthi, Xiangyu Ke, Arijit Khan	Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution.
37	Reply With: Proactive Recommendation of Email Attachments	Christophe Van Gysel, Bhaskar Mitra, Matteo Venanzi, Roy Rosemarin, Grzegorz Kukla, Piotr Grudzien, Nicola Cancedda	In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user.
38	Learning and Transferring Social and Item Visibilities for Personalized Recommendation	Lin Xiao, Zhang Min, Zhang Yongfeng, Liu Yiqun, Ma Shaoping	In this paper, we propose a novel user preference model for recommender systems that considers the visibility of both items and social relationships.
39	Joint Topic-Semantic-aware Social Recommendation for Online Voting	Hongwei Wang, Jia Wang, Miao Zhao, Jiannong Cao, Minyi Guo	In this paper, we investigate how to utilize these two factors in a comprehensive manner when doing voting recommendation.
40	Interactive Social Recommendation	Xin Wang, Steven C.H. Hoi, Chenghao Liu, Martin Ester	In the real world, new users may leave the systems for the reason of being recommended with boring items before enough data is collected for training a good model, which results in an inefficient customer retention.
41	From Properties to Links: Deep Network Embedding on Incomplete Graphs	Dejian Yang, Senzhang Wang, Chaozhuo Li, Xiaoming Zhang, Zhoujun Li	In this paper, we for the first time study the problem of network embedding on incomplete networks.
42	Learning Community Embedding with Community Detection and Node Embedding on Graphs	Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria	In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes.
43	Attributed Network Embedding for Learning in a Dynamic Environment	Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu	In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework – DANE.
44	Learning Node Embeddings in Interaction Graphs	Yao Zhang, Yun Xiong, Xiangnan Kong, Yangyong Zhu	In this paper, we study the problem of node embedding in attributed interaction graphs.
45	Efficient Computation of Subspace Skyline over Categorical Domains	Md Farhadur Rahman, Abolfazl Asudeh, Nick Koudas, Gautam Das	In this paper, we place the problem of skyline discovery over categorical attributes into perspective and design efficient algorithms for two cases.
46	Fast Algorithms for Pareto Optimal Group-based Skyline	Wenhui Yu, Zheng Qin, Jinfei Liu, Li Xiong, Xu Chen, Huidi Zhang	To address this gap, we study the skyline computation in group case and propose fast methods to find the group-based skyline (G-skyline), which contains Pareto optimal groups.
47	Probabilistic Skyline on Incomplete Data	Kaiqi Zhang, Hong Gao, Xixian Han, Zhipeng Cai, Jianzhong Li	In this paper, we propose a novel skyline definition utilizing probabilistic model on incomplete data where each point has a probability to be in the skyline.
48	Communication-Efficient Distributed Skyline Computation	Haoyu Zhang, Qin Zhang	In this paper we study skyline queries in the distributed computational model, where we have s remote sites and a central coordinator; each site holds a piece of data, and the coordinator wants to compute the skyline of the union of the s datasets.
49	Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary	Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal	We describe the overall design and architecture of the statistical modeling system underlying this product.
50	Efficient Document Filtering Using Vector Space Topic Expansion and Pattern-Mining: The Case of Event Detection in Microposts	Julia Proskurnia, Ruslan Mavlyutov, Carlos Castillo, Karl Aberer, Philippe Cudré-Mauroux	In this paper, we propose a robust and effective approach to automatically identify microposts related to a specific topic defined by a small sample of reference documents.
51	LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity	Changsha Ma, Zhisheng Yan, Chang Wen Chen	In this paper, we aim to achieve fast prediction of long-term video popularity in the complex YouTube networks.
52	Modeling Affinity based Popularity Dynamics	Minkyoung Kim, Daniel A. McFarland, Jure Leskovec	In this study, we propose the Affinity Poisson Process model (APP) which models popularity dynamics, by incorporating (1) affinities between subgroups, (2) heterogeneous preferential attachment, and (3) subgroup-level time decay.
53	Scenic Routes Now: Efficiently Solving the Time-Dependent Arc Orienteering Problem	Ying Lu, Gregor Josse, Tobias Emrich, Ugur Demiryurek, Matthias Renz, Cyrus Shahabi, Matthias Schubert	Therefore, we propose an efficient approximate solution with spatial pruning techniques, optimized for fast response systems. In this paper, we introduce a novel problem called Twofold Time-Dependent Arc Orienteering Problem (2TD-AOP), which seeks to find a path from a source to a destination maximizing an accumulated value (e.g., attractiveness of the path) while not exceeding a cost budget (e.g., total travel time).
54	Modeling Temporal-Spatial Correlations for Crime Prediction	Xiangyu Zhao, Jiliang Tang	In this paper, we exploit temporal-spatial correlations in urban data for crime prediction.
55	Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data	Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu, Naren Ramakrishnan	To handle missing values, we propose a data tensor completion method based on price domain knowledge.
56	Exploiting Spatio-Temporal User Behaviors for User Linkage	Wei Chen, Hongzhi Yin, Weiqing Wang, Lei Zhao, Wen Hua, Xiaofang Zhou	To tackle the problem, we propose a novel model STUL (Spatio-Temporal User Linkage) that consists of the following two components.
57	Similarity-based Distant Supervision for Definition Retrieval	Jiepu Jiang, James Allan	We present a distant supervision approach addressing this challenge without using explicitly labeled data.
58	Hybrid BiLSTM-Siamese network for FAQ Assistance	Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan	We describe an automated assistant for answering frequently asked questions; our system has been deployed, and is currently answering HR-related queries in two different areas (leave management and health insurance) to a large number of users.
59	Regularized and Retrofitted models for Learning Sentence Representation with Context	Tanay Kumar Saha, Shafiq Joty, Naeemul Hassan, Mohammad Al Hasan	We evaluate our sentence representation models in a setup, where context is available to infer sentence vectors.
60	Talking to Your TV: Context-Aware Voice Search with Hierarchical Recurrent Neural Networks	Jinfeng Rao, Ferhan Ture, Hua He, Oliver Jojic, Jimmy Lin	We tackle the novel problem of navigational voice queries posed against an entertainment system, where viewers interact with a voice-enabled remote controller to specify the TV program to watch.
61	GPU-Accelerated Graph Clustering via Parallel Label Propagation	Yusuke Kozawa, Toshiyuki Amagasa, Hiroyuki Kitagawa	To this end, this paper proposes a fast graph clustering method using GPUs.
62	Temporally Like-minded User Community Identification through Neural Embeddings	Hossein Fani, Ebrahim Bagheri, Weichang Du	We propose a neural embedding approach to identify temporally like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest.
63	Community-Based Network Alignment for Large Attributed Network	Zheng Chen, Xinli Yu, Bo Song, Jianliang Gao, Xiaohua Hu, Wei-Shih Yang	In this paper, based on Stochastic Block Model (SBM) and Dirichlet-multinomial, we propose "divide-and-conquer" models CAlign that jointly consider network alignment, community discovery and community alignment in one framework for large networks with node attributes, in an effort to reduce both the computation time and memory usage while achieving better or competitive performance.
64	A Non-negative Symmetric Encoder-Decoder Approach for Community Detection	Bing-Jie Sun, Huawei Shen, Jinhua Gao, Wentao Ouyang, Xueqi Cheng	In this paper, we propose a non-negative symmetric encoder-decoder approach for community detection.
65	Fast Word Recognition for Noise channel-based Models in Scenarios with Noise Specific Domain Knowledge	Marco Cristo, Raíza Hanada, André Carvalho, Fernando Anglada Lores, Maria da Graça C. Pimentel	In this work, we propose very efficient methods for word recognition in very noisy scenarios which support effective edit-based distance algorithms in a Mor-Fraenkel index, searchable using a minimum perfect hashing.
66	Detecting Multiple Periods and Periodic Patterns in Event Time Sequences	Quan Yuan, Jingbo Shang, Xin Cao, Chao Zhang, Xinhe Geng, Jiawei Han	In this paper, we study the problem of discovering all true periods and the corresponded occurring patterns of an event from a noisy and incomplete observation sequence.
67	Finding Periodic Discrete Events in Noisy Streams	Abhirup Ghosh, Christopher Lucas, Rik Sarkar	We describe a model of periodic events that covers both idealized and realistic scenarios characterized by multiple kinds of noise.
68	Fast and Accurate Time Series Classification with WEASEL	Patrick Schäfer, Ulf Leser	In this paper, we present WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is both fast and accurate.
69	QLever: A Query Engine for Efficient SPARQL+Text Search	Hannah Bast, Björn Buchhold	We present QLever, a query engine for efficient combined search on a knowledge base and a text corpus, in which named entities from the knowledge base have been identified (that is, recognized and disambiguated).
70	A Study of Main-Memory Hash Joins on Many-core Processor: A Case with Intel Knights Landing Architecture	Xuntao Cheng, Bingsheng He, Xiaoli Du, Chiew Tong Lau	In this paper, we experimentally revisit the state-of-the-art main-memory hash join algorithms to study how the new hardware features of KNL affect the algorithmic design and tuning as well as to identify the opportunities for further performance improvement on KNL.
71	PQBF: I/O-Efficient Approximate Nearest Neighbor Search by Product Quantization	Yingfan Liu, Hong Cheng, Jiangtao Cui	In this paper, we propose an I/O-efficient PQ based solution for ANN search.
72	ANS-Based Index Compression	Alistair Moffat, Matthias Petri	Here we combine the recently developed "asymmetric numeral systems" (ANS) approach to entropy coding and a range of previous index compression methods, including VByte, Simple, and Packed.
73	Covering the Optimal Time Window Over Temporal Data	Bin Cao, Chenyu Hou, Jing Fan	In this paper, we propose a new problem: covering the optimal time window over temporal data.
74	Scaling Probabilistic Temporal Query Evaluation	Melisachew Wudage Chekol	In this work, we propose the PRATiQUE (PRobAbilistic Temporal QUery Evaluation) framework for scalable temporal query evaluation.
75	Efficient Discovery of Abnormal Event Sequences in Enterprise Security Systems	Boxiang Dong, Zhengzhang Chen, Hui (Wendy) Wang, Lu-An Tang, Kai Zhang, Ying Lin, Zhichun Li, Haifeng Chen	In this work, we formulate a novel problem in intrusion detection – suspicious event sequence discovery, and propose GID, an efficient graph-based intrusion detection technique that can identify abnormal event sequences from massive heterogeneous process traces with high accuracy.
76	Temporal Analog Retrieval using Transformation over Dual Hierarchical Structures	Yating Zhang, Adam Jatowt, Katsumi Tanaka	In this paper, we provide a general framework to bridge different domains across-time and, by this, to facilitate search and comparison as if carried in user’s familiar domain (i.e., the present).
77	Does That Mean You’re Happy?: RNN-based Modeling of User Interaction Sequences to Detect Good Abandonment	Kyle Williams, Imed Zitouni	In this paper, we investigate how sequences of user interactions on the SERP differ between good and bad abandonment.
78	Deep Sequential Models for Task Satisfaction Prediction	Rishabh Mehrotra, Ahmed Hassan Awadallah, Milad Shokouhi, Emine Yilmaz, Imed Zitouni, Ahmed El Kholy, Madian Khabsa	In this work we go beyond such atomic tasks and consider the problem of predicting user’s satisfaction when engaged in complex search tasks composed of many different queries and subtasks.
79	Adaptive Persistence for Search Effectiveness Measures	Jiepu Jiang, James Allan	In contrast, we present work that adapts the persistence factor according to the ranking and relevance of the ranked lists being evaluated.
80	Beyond Success Rate: Utility as a Search Quality Metric for Online Experiments	Widad Machmouchi, Ahmed Hassan Awadallah, Imed Zitouni, Georg Buscher	In this work, we propose the use of utility as a measure of searcher satisfaction.
81	Linking News across Multiple Streams for Timeliness Analysis	Ida Mele, Seyed Ali Bahrainian, Fabio Crestani	In this paper, we propose techniques for cross-linking news streams based on the reported events with the purpose of analyzing the temporal dependencies among streams.
82	Growing Story Forest Online from Massive Breaking News	Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu	We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures in an online fashion.
83	iFACT: An Interactive Framework to Assess Claims from Tweets	Wee Yong Lim, Mong Li Lee, Wynne Hsu	In this work, we present an interactive framework called iFACT for assessing the credibility of claims from tweets.
84	CSI: A Hybrid Deep Model for Fake News Detection	Natali Ruchansky, Sungyong Seo, Yan Liu	In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction.
85	Selective Value Coupling Learning for Detecting Outliers in High-Dimensional Categorical Data	Guansong Pang, Hongzuo Xu, Longbing Cao, Wentao Zhao	This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data.
86	Outlier Detection in Sparse Data with Factorization Machines	Mengxiao Zhu, Charu C. Aggarwal, Shuai Ma, Hui Zhang, Jinpeng Huai	In this study, we propose an outlier detection approach for sparse data with factorization machines.
87	Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning	Xian Teng, Yu-Ru Lin, Xidao Wen	We propose a Multi-view Time-Series Hypersphere Learning (MTHL) approach that leverages multi-view learning and support vector description to tackle this problem.
88	A Fast Trajectory Outlier Detection Approach via Driving Behavior Modeling	Hao Wu, Weiwei Sun, Baihua Zheng	Motivated by this, we propose a vehicle outlier detection approach namely DB-TOD which is based on probabilistic model via modeling the driving behavior/preferences from the set of historical trajectories.
89	BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion	Jiawei Zhang, Limeng Cui, Philip S. Yu, Yuanhua Lv	In this paper, we propose to detect the social communities of the employees in companies based on the broad learning setting with both these online and offline information sources simultaneously, and the problem is formally called the "Broad Learning based Enterprise Community Detection" (BL-ECD) problem.
90	Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks	Tuan-Anh Hoang, Ee-Peng Lim	In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks.
91	To Be Connected, or Not to Be Connected: That is the Minimum Inefficiency Subgraph Problem	Natali Ruchansky, Francesco Bonchi, David Garcia-Soriano, Francesco Gullo, Nicolas Kourtellis	We study the problem of extracting a selective connector for a given set of query vertices Q subset of V in a graph G = (V,E).
92	MGAE: Marginalized Graph Autoencoder for Graph Clustering	Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang	In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
93	BoostVHT: Boosting Distributed Streaming Decision Trees	Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales	This paper introduces BoostVHT, a technique to parallelize online boosting algorithms.
94	Stream Aggregation Through Order Sampling	Nick Duffield, Yunhong Xu, Liangzhen Xia, Nesreen K. Ahmed, Minlan Yu	This paper introduces a new single-pass reservoir weighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA).
95	FUSION: An Online Method for Multistream Classification	Ahsanul Haque, Zhuoyi Wang, Swarup Chandra, Bo Dong, Latifur Khan, Kevin W. Hamlen	In this paper, we propose an efficient solution for multistream classification by fusing drift detection into online data shift adaptation.
96	Maintaining Densest Subsets Efficiently in Evolving Hypergraphs	Shuguang Hu, Xiaowei Wu, T-H. Hubert Chan	In this paper we study the densest subgraph problem, which plays a key role in many graph mining applications.
97	Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services	Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun, Philip S. Yu	In this work, we forecast order response time on current day by fusing data from order history and driver historical locations.
98	Tensor Rank Estimation and Completion via CP-based Nuclear Norm	Qiquan Shi, Haiping Lu, Yiu-ming Cheung	Several Bayesian solutions have been proposed but they often under/over-estimate the tensor rank while being quite slow.
99	Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract	Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang	This work proposed a method called onlineCP-ALS to incrementally update tensor component matrices, followed by a self-tuning one-class support vector machine for online damage identification.
100	Collaborative Filtering as a Case-Study for Model Parallelism on Bulk Synchronous Systems	Ariyam Das, Ishan Upadhyaya, Xiangrui Meng, Ameet Talwalkar	Using collaborative filtering as a case-study, we introduce an efficient model parallel industrial scale algorithm for alternating least squares (ALS), along with a highly optimized implementation of ALS that serves as the default implementation in MLlib, Apache Spark’s machine learning library.
101	Modeling Student Learning Styles in MOOCs	Yuling Shi, Zhiyong Peng, Hongning Wang	In this work, based on a thorough qualitative study of students’ behaviors recorded in two MOOC courses with large student enrollments, we develop a non-parametric Bayesian model to capture students’ sequential learning activities in a generative manner.
102	Tracking Knowledge Proficiency of Students with Educational Priors	Yuying Chen, Qi Liu, Zhenya Huang, Le Wu, Enhong Chen, Runze Wu, Yu Su, Guoping Hu	To this end, in this paper, we devise an explanatory probabilistic approach to track the knowledge proficiency of students over time by leveraging educational priors.
103	Spreadsheet Property Detection With Rule-assisted Active Learning	Zhe Chen, Sasha Dadiomov, Richard Wesley, Gang Xiao, Daniel Cory, Michael Cafarella, Jock Mackinlay	In this paper, we focus on the problem of spreadsheet property detection.
104	Learning Knowledge Embeddings by Combining Limit-based Scoring Loss	Xiaofei Zhou, Qiannan Zhu, Ping Liu, Li Guo	Learning Knowledge Embeddings by Combining Limit-based Scoring Loss
105	Length Adaptive Recurrent Model for Text Classification	Zhengjie Huang, Zi Ye, Shuangyin Li, Rong Pan	In this paper, we propose a Length Adaptive Recurrent Model (LARM) which can automatically determine the minimum text length that is necessary to perform the classification.
106	Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs	Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui	In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting.
107	Movie Fill in the Blank with Adaptive Temporal Attention and Description Update	Jie Chen, Jie Shao, Fumin Shen, Chengkun He, Lianli Gao, Heng Tao Shen	To address this problem, in this paper we propose to use a novel LSTM network called LSTM with Linguistic gate (LSTMwL), which exploits adaptive temporal attention for MovieFIB.
108	Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media	Rupinder Paul Khandpur, Taoran Ji, Steve Jan, Gang Wang, Chang-Tien Lu, Naren Ramakrishnan	We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks.
109	Budgeted Task Scheduling for Crowdsourced Knowledge Acquisition	Tao Han, Hailong Sun, Yangqiu Song, Zizhe Wang, Xudong Liu	In this paper, we present a new framework for task scheduling with the limited budget, targeting an effective solution to more specific knowledge acquisition.
110	Hyper Questions: Unsupervised Targeting of a Few Experts in Crowdsourcing	Jiyi Li, Yukino Baba, Hisashi Kashima	In this paper, we focus on an important class of answer aggregation problems in which majority voting fails and propose the concept of hyper questions to devise effective aggregation methods.
111	Modeling Menu Bundle Designs of Crowdfunding Projects	Yusan Lin, Peifeng Yin, Wang-Chien Lee	In this paper, we raise a novel research question: understanding project creators’ decisions of reward designs to level their chance to succeed.
112	Forecasting Ad-Impressions on Online Retail Websites using Non-homogeneous Hawkes Processes	Krunal Parmar, Samuel Bushi, Sourangshu Bhattacharya, Surender Kumar	In this paper, we study the problem of predicting user visits or potential ad-impressions to online retail websites, based on historical time-stamps.
113	Volume Ranking and Sequential Selection in Programmatic Display Advertising	Yuxuan Song, Kan Ren, Han Cai, Weinan Zhang, Yong Yu	In this paper, we borrow in the idea of top-N ranking and filtering techniques from information retrieval and propose an effective ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering while maximizing campaign efficiency.
114	On Migratory Behavior in Video Consumption	Huan Yan, Tzu-Heng Lin, Gang Wang, Yong Li, Haitao Zheng, Depeng Jin, Ben Y. Zhao	In this paper, we take a data-driven approach to analyze and model user migration behavior in video streaming, i.e., users switching content provider during active sessions.
115	FM-Hawkes: A Hawkes Process Based Approach for Modeling Online Activity Correlations	Sha Li, Xiaofeng Gao, Weiming Bao, Guihai Chen	In this work, we target this new problem by modeling the interplay between the time series of different types of activities and apply our model to predict future user behavior.
116	Deep Learning Based Forecasting of Critical Infrastructure Data	Zahra Zohrevand, Uwe Glässer, Mohammad A. Tayebi, Hamed Yaghoubi Shahir, Mehdi Shirmaleki, Amir Yaghoubi Shahir	In this paper we propose a novel deep learning based framework for time series analysis and prediction by ensembling parametric and nonparametric methods.
117	Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information	Wonsung Lee, Kyungwoo Song, Il-Chul Moon	This paper presents variational approaches for collaborative filtering to deal with auxiliary information.
118	DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades	Qi Cao, Huawei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng	In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.
119	CNN-IETS: A CNN-based Probabilistic Approach for Information Extraction by Text Segmentation	Meng Hu, Zhixu Li, Yongxin Shen, An Liu, Guanfeng Liu, Kai Zheng, Lei Zhao	Information Extraction by Text Segmentation (IETS) aims at segmenting text inputs to extract implicit data values contained in them.The state-of-art IETS approaches mainly rely on machine learning techniques, either supervised or unsupervised.However, while the supervised approaches require a large labelled training data, the performance of the unsupervised ones could be unstable on different data sets.To overcome their weaknesses, this paper introduces CNN-IETS, a novel unsupervised probabilistic approach that takes the advantages of pre-existing data and a Convolution Neural Network (CNN)-based probabilistic classification model.
120	A Personalized Predictive Framework for Multivariate Clinical Time Series via Adaptive Model Selection	Zitao Liu, Milos Hauskrecht	To address these problems we propose, develop and experiment with a new adaptive forecasting framework for building multivariate clinical time series models for a patient and for supporting patient-specific predictions.
121	DiagTree: Diagnostic Tree for Differential Diagnosis	Yejin Kim, Jingyun Choi, Yosep Chong, Xiaoqian Jiang, Hwanjo Yu	We propose a Diagnostic Tree (DiagTree), a new framework for diagnosing diseases, which combines several tests to reduce the diagnosis time and to incorporate real-world constraints into discrete optimization.
122	Fine-grained Patient Similarity Measuring using Deep Metric Learning	Jiazhi Ni, Jie Liu, Chenxin Zhang, Dan Ye, Zhirou Ma	In this paper, we present a novel three layer patient similarity deep metric learning framework (PSDML) by optimizing quadruple loss improved from triplet loss, to learn an embedding distance for disease classification among the patients.
123	Differentially Private Regression for Discrete-Time Survival Analysis	Thông T. Nguyên, Siu Cheung Hui	In this work, we aim to propose solutions for the regression problem in survival analysis with the protection of differential privacy which is a golden standard of privacy protection in data privacy research.
124	From Fingerprint to Footprint: Revealing Physical World Privacy Leakage by Cyberspace Cookie Logs	Huandong Wang, Chen Gao, Yong Li, Zhi-Li Zhang, Depeng Jin	In this paper we address the following fundamental question: what kind – and how much – of user physical world privacy might be leaked if we could get hold of such diverse network datasets even without any physical location information.
125	Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition	Lingjuan Lyu, Xuanli He, Yee Wei Law, Marimuthu Palaniswami	For collaborative learning, we proposed a novel LSTM-CNN model combining the merits of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).
126	Privacy Aware Temporal Profiling of Emails in Distributed Setup	Sutapa Mondal, Manish Shukla, Sachin Lodha	In this paper, we propose a system for building an individual’s perceived knowledge profile "What she knows?" )
127	Name Disambiguation in Anonymized Graphs using Network Embedding	Baichuan Zhang, Mohammad Al Hasan	In this work, we propose a novel name disambiguation method.
128	Weakly-Guided User Stance Prediction via Joint Modeling of Content and Social Interaction	Rui Dong, Yizhou Sun, Lu Wang, Yupeng Gu, Yuan Zhong	In this work, we present a weakly-guided user stance modeling framework which simultaneously considers two types of information: what do you say (via stance-based content generative model) and how do you behave (via social interaction-based graph regularization).
129	Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies	Yujie Fan, Yiming Zhang, Yanfang Ye, Xin li, Wanhong Zheng	In this paper, we propose a novel framework named AutoDOA to automatically detect the opioid addicts from Twitter, which can potentially assist in sharpening our understanding toward the behavioral process of opioid abuse and addiction.
130	Understanding and Predicting Weight Loss with Mobile Social Networking Data	Zhiwei Wang, Tyler Derr, Dawei Yin, Jiliang Tang	In this paper, we conduct the initial investigation to understand weight loss with a large-scale mobile social networking dataset with near 10 million users.
131	Tweet Geolocation: Leveraging Location, User and Peer Signals	Wen-Haw Chong, Ee-Peng Lim	We propose several models that leverage on three types of signals from locations, users and peers.
132	A Two-step Information Accumulation Strategy for Learning from Highly Imbalanced Data	Bin Liu, Min Zhang, Weizhi Ma, Xin Li, Yiqun Liu, Shaoping Ma	In this paper, Our major point is that the imbalance is the observed phenomenon but not the cause of the problem.
133	Understanding Database Performance Inefficiencies in Real-world Web Applications	Cong Yan, Alvin Cheung, Junwen Yang, Shan Lu	In this paper, we studied 27 real-world open-source applications built on top of the popular Ruby on Rails ORM framework, with the goal to understand the database-related performance inefficiencies in these applications.
134	Data Driven Chiller Plant Energy Optimization with Domain Knowledge	Hoang Dung Vu, Kok Soon Chai, Bryan Keating, Nurislam Tursynbek, Boyan Xu, Kaige Yang, Xiaoyan Yang, Zhenjie Zhang	This paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants.
135	Partitioning Orders in Online Shopping Services	Sreenivas Gollapudi, Ravi Kumar, Debmalya Panigrahy, Rina Panigrahy	Formulating this as an optimization problem, we propose a family of simple and efficient algorithms that admit natural constraints such as number of items a shopper can process in this setting.
136	Taxonomy Induction Using Hypernym Subsequences	Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer	We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms.
137	Unsupervised Concept Categorization and Extraction from Scientific Document Titles	Adit Krishnan, Aravind Sankar, Shi Zhi, Jiawei Han	Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.).
138	MIKE: Keyphrase Extraction by Integrating Multidimensional Information	Yuxiang Zhang, Yaocheng Chang, Xiaoqing Liu, Sujatha Das Gollapalli, Xiaoli Li, Chunjing Xiao	In this paper, we focus on how to effectively exploit multidimensional information to improve the keyphrase extraction performance (MIKE).
139	QALink: Enriching Text Documents with Relevant Q&A Site Contents	Yixuan Tang, Weilong Huang, Qi Liu, Anthony K.H. Tung, Xiaoli Wang, Jisong Yang, Beibei Zhang	In this paper, we devise a rigorous formulation of the novel text enrichment problem, and design an end-to-end system named QALink which assigns the most relevant Q&A contents to the corresponding section of the document.
140	Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory	Yanan Zheng, Lijie Wen, Jianmin Wang, Jun Yan, Lei Ji	In this paper, we propose a Hierarchical Deep Generative Model With Dual Memory to address the two challenges.
141	Active Learning for Large-Scale Entity Resolution	Kun Qian, Lucian Popa, Prithviraj Sen	In this paper, we introduce an active learning system that learns, at scale, multiple rules each having significant coverage of the space of duplicates, thus leading to high recall, in addition to high-precision.
142	Indexable Bayesian Personalized Ranking for Efficient Top-k Recommendation	Dung D. Le, Hady W. Lauw	In this paper, we introduce Indexable Bayesian Personalized Ranking (IBPR) that learns from ordinal preference to produce representation that is inherently compatible with the aforesaid indices.
143	Latency Reduction via Decision Tree Based Query Construction	Aman Grover, Dhruv Arya, Ganesh Venkataraman	We present a way to model the underlying complex ranking function via decision trees.
144	Broad Learning based Multi-Source Collaborative Recommendation	Junxing Zhu, Jiawei Zhang, Lifang He, Quanyuan Wu, Bin Zhou, Chenwei Zhang, Philip S. Yu	In this paper, we focus on studying the recommendation problem that can provide ratings of items or services.
145	Neural Attentive Session-based Recommendation	Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma	In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem.
146	A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation	Jarana Manotumruksa, Craig Macdonald, Iadh Ounis	In this paper, we propose a Deep Recurrent Collaborative Filtering framework (DRCF) with a pairwise ranking function that aims to capture user-venue interactions in a CF manner from sequences of observed feedback by leveraging Multi-Layer Perception and Recurrent Neural Network architectures.
147	Recommendation with Capacity Constraints	Konstantina Christakopoulou, Jaya Kawale, Arindam Banerjee	Towards closing this gap, we propose Recommendation with Capacity Constraints — a framework that optimizes for both recommendation accuracy and expected item usage that respects the capacity constraints.
148	Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources	Yongfeng Zhang, Qingyao Ai, Xu Chen, W. Bruce Croft	In this work, we propose a Joint Representation Learning (JRL) framework for top-N recommendation.
149	Interacting Attention-gated Recurrent Networks for Recommendation	Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M.J. Tax	In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.
150	A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation	Jarana Manotumruksa, Craig Macdonald, Iadh Ounis	Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR.
151	BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network	Daizong Ding, Mi Zhang, Shao-Yuan Li, Jie Tang, Xiaotie Chen, Zhi-Hua Zhou	In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend recommendation in social networks.
152	A Topic Model Based on Poisson Decomposition	Haixin Jiang, Rui Zhou, Limeng Zhang, Hua Wang, Yanchun Zhang	Based on the validity of the test on a claim that the data conforms to Poisson distribution we propose Poisson decomposition model (PDM), a statistical model for modeling count data of text corpora, which can straightly capture each document’s multidimensional numerical characteristics on topics.
153	A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings	Rui Wang, Wei Liu, Chris McDonald	We present a novel recurrent computational mechanism that specifically learns the compositionality by encoding the compositional rule of each word into a matrix.
154	Words are Malleable: Computing Semantic Shifts in Political and Media Discourse	Hosein Azarbonyad, Mostafa Dehghani, Kaspar Beelen, Alexandra Arkut, Maarten Marx, Jaap Kamps	We propose an approach for detecting semantic shifts between different viewpoints—broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party.
155	A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation	Gaurav Singh, Iain J. Marshall, James Thomas, John Shawe-Taylor, Byron C. Wallace	We propose a novel neural model that addresses these challenges.
156	Sybil Defense in Crowdsourcing Platforms	Dong Yuan, Guoliang Li, Qi Li, Yudian Zheng	To address this problem, we propose a sybil defense framework for crowdsourcing, which can help crowdsourcing platforms to identify sybil workers and defense the sybil attack.
157	HoloScope: Topology-and-Spike Aware Fraud Detection	Shenghua Liu, Bryan Hooi, Christos Faloutsos	Hence, we propose HoloScope, which introduces a novel metric "contrast suspiciousness" integrating information from graph topology and spikes to more accurately detect fraudulent users and objects.
158	Building a Dossier on the Cheap: Integrating Distributed Personal Data Resources Under Cost Constraints	Imrul Chowdhury Anindya, Harichandan Roy, Murat Kantarcioglu, Bradley Malin	Thus, in this work, we investigate a novel privacy risk assessment framework, based on adversaries who plan an integration of datasets for the most accurate estimate of targeted sensitive attributes under a certain budget.
159	DeMalC: A Feature-rich Machine Learning Framework for Malicious Call Detection	Yuhong Li, Dongmei Hou, Aimin Pan, Zhiguo Gong	In this work, we propose a solution named DeMalC to address those problems by applying the machine learning algorithmm on a novel set of discriminative features.
160	FA*IR: A Fair Top-k Ranking Algorithm	Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates	In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n » k candidates, maximizing utility (i.e., select the "best" candidates) subject to group fairness criteria.
161	Capturing Feature-Level Irregularity in Disease Progression Modeling	Kaiping Zheng, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, Wei Luen James Yip	To handle this issue, we propose a model based on the Gated Recurrent Unit by decaying the effect of previous records using fine-grained feature-level time span information, and learn the decaying parameters for different features to take into account their different behaviours like decaying speeds under irregularity.
162	Health Forum Thread Recommendation Using an Interest Aware Topic Model	Kishaloy Halder, Min-Yen Kan, Kazunari Sugiyama	We introduce a general, interest-aware topic model (IATM), in which known higher-level interests on topics expressed by each user can be modeled.
163	HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks	Liangzhe Chen, Xinfeng Xu, Sangkeun Lee, Sisi Duan, Alfonso G. Tarditi, Supriya Chinthavali, B. Aditya Prakash	In this paper, we study this problem using a heterogeneous network viewpoint.
164	SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process	Lucky Dhakad, Mrinal Das, Chiranjib Bhattacharyya, Samik Datta, Mihir Kale, Vivek Mehta	SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process
165	Semi-Supervised Event-related Tweet Identification with Dynamic Keyword Generation	Xin Zheng, Aixin Sun, Sibo Wang, Jialong Han	In this paper, we propose a semi-supervised method to obtain high quality event-related tweets from Twitter stream, in terms of precision and recall.
166	Distant Meta-Path Similarities for Text-Based Heterogeneous Information Networks	Chenguang Wang, Yangqiu Song, Haoran Li, Yizhou Sun, Ming Zhang, Jiawei Han	In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity.
167	Unsupervised Feature Selection with Joint Clustering Analysis	Shuai An, Jun Wang, Jinmao Wei, Zhenglu Yang	To address the problems, we propose a novel unsupervised approach that integrates sparse feature selection and robust joint clustering analysis.
168	Multi-Label Feature Selection using Correlation Information	Ali Braytee, Wei Liu, Daniel R. Catchpoole, Paul J. Kennedy	In this paper, we propose a CMFS (Correlated- and Multi-label Feature Selection method), based on non-negative matrix factorization (NMF) for simultaneously performing feature selection and addressing the aforementioned challenges.
169	Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation	Yiyang Li, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang	In this paper, we consider to transfer knowledge from a larger text corpus.
170	NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation	Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li	In this paper, we propose a deep neural network model to effectively measure the semantic matching between mention’s context and target entity.
171	Relaxing Graph Pattern Matching With Explanations	Jia Li, Yang Cao, Shuai Ma	These together give us a framework for enriching the results of graph pattern matching.
172	Active Network Alignment: A Matching-Based Approach	Eric Malmi, Aristides Gionis, Evimaria Terzi	This paper introduces two novel relative-query strategies, TopMatchings and GibbsMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem.
173	Discovering Graph Temporal Association Rules	Mohammad Hossein Namaki, Yinghui Wu, Qi Song, Peng Lin, Tingjian Ge	This paper proposes graph temporal association rules (GTAR).
174	Minimizing Tension in Teams	Behzad Golshan, Evimaria Terzi	The question we consider in this paper is the following: "can this tension be reduced by providing incentives to individuals to change their work habits?"
175	Interactive Spatial Keyword Querying with Semantics	Jiabao Sun, Jiajie Xu, Kai Zheng, Chengfei Liu	To overcome this flaw, this paper investigates the interactive spatial keyword querying with semantics.
176	From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach	Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta, Shakti Sinha	This paper describes our approach to solving these challenges.
177	Learning to Attend, Copy, and Generate for Session-Based Query Suggestion	Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, Pascal Fleury	In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion.
178	Deep Context Modeling for Web Query Entity Disambiguation	Zhen Liao, Xinying Song, Yelong Shen, Saekoo Lee, Jianfeng Gao, Ciya Liao	In this paper, we presented a new study for Web query entity disambiguation (QED), which is the task of disambiguating different candidate entities in a knowledge base given their mentions in a query.
179	An Attention-based Collaboration Framework for Multi-View Network Representation Learning	Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han	We propose a multi-view representation learning approach, which promotes the collaboration of different views and lets them vote for the robust representations.
180	Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations	Zhen Tan, Xiang Zhao, Wei Wang	In this paper, we propose a novel knowledge graph embedding model, CombinE.
181	Learning Edge Representations via Low-Rank Asymmetric Projections	Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou	We propose a new method for embedding graphs while preserving directed edge information.
182	HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning	Tao-yang Fu, Wang-Chien Lee, Zhen Lei	In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs).
183	Core Decomposition and Densest Subgraph in Multilayer Networks	Edoardo Galimberti, Francesco Bonchi, Francesco Gullo	We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and show how multilayer core decomposition can be exploited to approximate this problem with quality guarantees.
184	Fully Dynamic Algorithm for Top-	Muhammad Anis Uddin Nasir, Aristides Gionis, Gianmarco De Francisci Morales, Sarunas Girdzijauskas	In this paper, we study the top-k densest-subgraph problem in the sliding-window model and propose an efficient fully-dynamic algorithm.
185	Minimizing Dependence between Graphs	Yu Rong, Hong Cheng	We propose two algorithms to solve GDM.
186	Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results	Mohamed Ghalwash, Ying Li, Ping Zhang, Jianying Hu	We propose a method that leverages drug information to find a meaningful list of drugs that have an effect on the laboratory result.
187	Efficient Discovery of Ontology Functional Dependencies	Sridevi Baskaran, Alexander Keller, Fei Chiang, Lukasz Golab, Jaroslaw Szlichta	Our technical contributions are twofold: 1) theoretical foundations for OFDs, including a set of sound and complete axioms and a linear-time inference procedure, and 2) an algorithm for discovering OFDs (exact ones and ones that hold with some exceptions) from data that uses the axioms to prune the exponential search space in the number of attributes.
188	Automatic Navbox Generation by Interpretable Clustering over Linked Entities	Chenhao Xie, Lihan Chen, Jiaqing Liang, Kezun Zhang, Yanghua Xiao, Hanghang Tong, Haixun Wang, Wei Wang	In this paper, we target on the automatic generation of Navbox for Wikipedia articles.
189	A Two-Stage Framework for Computing Entity Relatedness in Wikipedia	Marco Ponza, Paolo Ferragina, Soumen Chakrabarti	Introducing a new dataset with human judgments of entity relatedness, we present a thorough study of all entity relatedness measures in recent literature based on Wikipedia as the knowledge graph.
190	Incorporating the Latent Link Categories in Relational Topic Modeling	Yuan He, Cheng Wang, Changjun Jiang	In this paper, we introduce a latent correlation factor to categorize the links into several categories, and each category corresponds to a unique kind of association.
191	Tone Analyzer for Online Customer Service: An Unsupervised Model with Interfered Training	Peifeng Yin, Zhe Liu, Anbang Xu, Taiga Nakamura	In this work, by collecting and labeling online conversations of customer service on Twitter, we identify 8 new metrics, named as tones, to describe emotional information.
192	Nationality Classification Using Name Embeddings	Junting Ye, Shuchu Han, Yifan Hu, Baris Coskun, Meizhu Liu, Hong Qin, Steven Skiena	We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems.
193	Emotions in Social Networks: Distributions, Patterns, and Models	Shengmin Jin, Reza Zafarani	Based on our observations, we propose the Emotional-Tie model — a network model that can simulate the formation of friendships based on emotions.
194	Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases	Yan Zhuang, Guoliang Li, Zhuojian Zhong, Jianhua Feng	To achieve this goal, in this paper we propose a novel hybrid human-machine framework for large-scale KB integration.
195	Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems	Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi	In this work, we propose to improve long-term user engagement in a recommender system from the perspective of sequential decision optimization, where users’ click and return behaviors are directly modeled for online optimization.
196	Predicting Startup Crowdfunding Success through Longitudinal Social Engagement Analysis	Qizhen Zhang, Tengyuan Ye, Meryem Essaidi, Shivani Agarwal, Vincent Liu, Boon Thau Loo	In this paper, we perform a longitudinal data collection and analysis of AngelList – a popular crowdfunding social platform for connecting investors and entrepreneurs.
197	Optimizing Email Volume For Sitewide Engagement	Rupesh Gupta, Guanfeng Liang, Romer Rosales	In this paper we focus on the problem of optimizing email volume for maximizing sitewide engagement of an online social networking service.
198	Understanding Engagement through Search Behaviour	Mengdie Zhuang, Gianluca Demartini, Elaine G. Toms	In this paper, we investigate the potential to predict how users perceive engagement with search by modelling behavioural signals from log files using supervised learning methods.
199	Citation Metadata Extraction via Deep Neural Network-based Segment Sequence Labeling	Dong An, Liangcai Gao, Zhuoren Jiang, Runtao Liu, Zhi Tang	In this paper, we propose a sequence labeling model for citation metadata extraction, called segment sequence labeling.
200	A Novel Approach for Efficient Computation of Community Aware Ridesharing Groups	Samiul Anwar, Shuha Nabila, Tanzima Hashem	We propose a novel way to form ridesharing groups that reveals user social data in community levels, and ensures that a group member shares at least k common communities with at least other m members in the ridesharing group, where k and m are personalized parameters of every group member.
201	Extracting Entities of Interest from Comparative Product Reviews	Jatin Arora, Sumit Agrawal, Pawan Goyal, Sayan Pathak	This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites.
202	A Neural Collaborative Filtering Model with Interaction-based Neighborhood	Ting Bai, Ji-Rong Wen, Jun Zhang, Wayne Xin Zhao	Based on this consideration, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF).
203	Profiling DRDoS Attacks with Data Analytics Pipeline	Laure Berti-Equille, Yury Zhauniarovich	In this paper, we propose a first analytic pipeline that enables us to cluster and characterize attack campaigns into several main profiles that exhibit similarities.
204	A Compare-Aggregate Model with Dynamic-Clip Attention for Answer Selection	Weijie Bian, Si Li, Zhao Yang, Guang Chen, Zhiqing Lin	In this paper, unlike previous Compare-Aggregate models which utilize the traditional attention mechanism to generate corresponding word-level vector before comparison, we propose a novel attention mechanism named Dynamic-Clip Attention which is directly integrated into the Compare-Aggregate framework.
205	Learning Biological Sequence Types Using the Literature	Mohamed Reda Bouadjenek, Karin Verspoor, Justin Zobel	To address this problem of automatic sequence type classification, we propose the use of literature associated to sequence records as an external source of knowledge that can be leveraged for the classification task.
206	Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information	Chiyu Cai, Linjing Li, Daniel Zeng	The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior.
207	PMS: an Effective Approximation Approach for Distributed Large-scale Graph Data Processing and Mining	Yingjie Cao, Yangyang Zhang, Jianxin Li	In this paper, we propose an effective priority-based message sampling (PMS ) approach to further improve the performance of distributed graph processing at the cost of some accuracy loss.
208	Language Modeling by Clustering with Word Embeddings for Text Readability Assessment	Miriam Cha, Youngjune Gwon, H. T. Kung	We present a clustering-based language model using word embeddings for text readability prediction.
209	Compact Multiple-Instance Learning	Jing Chai, Weiwei Liu, Ivor W. Tsang, Xiaobo Shen	Two related issues might affect the performance of MIL algorithms: how to cope with label ambiguities and how to deal with non-discriminative components, and we propose COmpact MultiPle-Instance LEarning (COMPILE) to consider them simultaneously.
210	Text Embedding for Sub-Entity Ranking from User Reviews	Chih-Yu Chao, Yi-Fan Chu, Hsiu-Wei Yang, Chuan-Ju Wang, Ming-Feng Tsai	To deal with such analysis, we propose a text embedding framework for ranking sub-entities from user reviews of a given super-entity.
211	Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining	Elaheh Alipour Chavary, Sarah M. Erfani, Christopher Leckie	In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers.
212	Modeling Opinion Influence with User Dual Identity	Chengyao Chen, Zhitao Wang, Wenjie Li	In this work, we explore users’ dual identities, including both personal identities and social identities to build a more comprehensive opinion influence model for a better understanding of opinion behaviors.
213	An Empirical Analysis of Pruning Techniques: Performance, Retrievability and Bias	Ruey-Cheng Chen, Leif Azzopardi, Falk Scholer	In this paper, we investigate how the retrieval bias of a system changes as the inverted index is optimized for efficiency through static index pruning.
214	Text Coherence Analysis Based on Deep Neural Network	Baiyun Cui, Yingming Li, Yaqing Zhang, Zhongfei Zhang	In this paper, we propose a novel deep coherence model (DCM) using a convolutional neural network architecture to capture the text coherence.
215	Unsupervised Matrix-valued Kernel Learning For One Class Classification	Shaobo Dang, Xiongcai Cai, Yang Wang, Jianjia Zhang, Fang Chen	This paper is concerned with the one class classification(OCC) problem.
216	Analysis of Telegram, An Instant Messaging Service	Arash Dargahi Nobari, Negar Reshadatmand, Mahmood Neshati	In this paper, we developed a crawler to gather its public data.
217	Estimating Event Focus Time Using Neural Word Embeddings	Supratim Das, Arunav Mishra, Klaus Berberich, Vinay Setty	We propose several estimators that leverage distributional event and time representations learned from large external document collections by adapting the word2vec paradigm.
218	Personalized Image Aesthetics Assessment	Xiang Deng, Chaoran Cui, Huidi Fang, Xiushan Nie, Yilong Yin	In this paper, we propose to model user aesthetic perceptions using a set of exemplar images from social media platforms, and realize personalized aesthetics assessment by transferring this knowledge to adapt the results of the trained generic model.
219	Efficient Fault-Tolerant Group Recommendation Using alpha-beta-core	Danhao Ding, Hui Li, Zhipeng Huang, Nikos Mamoulis	To address this issue, we model the fault-tolerant subspace clustering problem as a search problem on graphs and present an algorithm, GraphRec, based on the concept of α-ß-core.
220	On Discovering the Number of Document Topics via Conceptual Latent Space	Nghia Duong-Trung, Lars Schmidt-Thieme	In this paper, we study the concept of conceptual stability via nonnegative matrix factorization.
221	Chinese Named Entity Recognition with Character-Word Mixed Embedding	Shijia E, Yang Xiang	To solve this issue, we propose a Chinese NER method based on Character-Word Mixed Embedding (CWME), and the method is in accord with the pipeline of Chinese natural language processing.
222	An Empirical Study of Embedding Features in Learning to Rank	Faezeh Ensan, Ebrahim Bagheri, Amal Zouaq, Alexandre Kouznetsov	We have extensively introduced and investigated the effectiveness of features learnt based on word and document embeddings to represent both queries and documents.
223	Privacy of Hidden Profiles: Utility-Preserving Profile Removal in Online Forums	Sedigheh Eslami, Asia J. Biega, Rishiraj Saha Roy, Gerhard Weikum	In this work, we investigate an alternative solution to standard profile removal, where posts of different users are split and merged into synthetic mediator profiles.
224	QoS-Aware Scheduling of Heterogeneous Servers for Inference in Deep Neural Networks	Zhou Fang, Tong Yu, Ole J. Mengshoel, Rajesh K. Gupta	This paper represents the QoS metric as a utility function of response delay and inference accuracy.
225	Geographic and Temporal Trends in Fake News Consumption During the 2016 US Presidential Election	Adam Fourney, Miklos Z. Racz, Gireeja Ranade, Markus Mobius, Eric Horvitz	We propose a simple model based on homophily in social networks to explain the linear association.
226	Inferring Appliance Energy Usage from Smart Meters using Fully Convolutional Encoder Decoder Networks	Felan Carlo C. Garcia, Erees Queen B. Macabebe	In this paper we present a method to provide appliance energy usage feedback from smart meters using energy disaggregation.
227	Tracking the Impact of Fact Deletions on Knowledge Graph Queries using Provenance Polynomials	Garima Gaur, Srikanta J. Bedathur, Arnab Bhattacharya	We propose a framework based on provenance polynomials to track the impact of knowledge graph changes on arbitrary SPARQL query results.
228	An Euclidean Distance based on the Weighted Self-information Related Data Transformation for Nominal Data Clustering	Lei Gu, Liying Zhang, Yang Zhao	This paper mainly aims to make the Euclidean distance measure appropriate to nominal data clustering, and the core idea is to transform each nominal attribute value into numerical.
229	Interest Diffusion in Heterogeneous Information Network for Personalized Item Ranking	Mukul Gupta, Pradeep Kumar, Rajhans Mishra	In this paper, we deal with the problem of the sparseness of data and accuracy of recommendations.
230	Source Retrieval for Web-Scale Text Reuse Detection	Matthias Hagen, Martin Potthast, Payam Adineh, Ehsan Fatehifar, Benno Stein	We propose a new approach that reaches a recall of~0.89—a performance gain of~51%.
231	Smart City Analytics: Ensemble-Learned Prediction of Citizen Home Care	Casper Hansen, Christian Hansen, Stephen Alstrup, Christina Lioma	We present an ensemble learning method that predicts large increases in the hours of home care received by citizens.
232	Fast K-means for Large Scale Clustering	Qinghao Hu, Jiaxiang Wu, Lu Bai, Yifan Zhang, Jian Cheng	In this paper, we propose a fast k-means algorithm named multi-stage k-means (MKM) which uses a multi-stage filtering approach.
233	Graph Ladder Networks for Network Classification	Ruiqi Hu, Shirui Pan, Jing Jiang, Guodong Long	In this paper, we propose an effective deep learning model, Graph Ladder Networks (GLN), for node classification in networks.
234	A Communication Efficient Parallel DBSCAN Algorithm based on Parameter Server	Xu Hu, Jun Huang, Minghui Qiu	In this paper, we propose PS-DBSCAN, a parallel DBSCAN algorithm that combines the disjoint-set data structure and Parameter Server framework, to minimize communication cost.
235	KIEM: A Knowledge Graph based Method to Identify Entity Morphs	Longtao Huang, Lin Zhao, Shangwen Lv, Fangzhou Lu, Yue Zhai, Songlin Hu	In this paper, we introduce a novel method based on knowledge graph, which takes advantage of both knowledge reasoning and statistic learning.
236	Ontology-based Graph Visualization for Summarized View	Xin Huang, Byron Choi, Jianliang Xu, William K. Cheung, Yanchun Zhang, Jiming Liu	In this paper, we study the problem of selecting a diverse set of k elements to summarize an input dataset with hierarchical terminologies, and visualize the summary in an ontology structure.
237	An Ad CTR Prediction Method Based on Feature Learning of Deep and Shallow Layers	Zai Huang, Zhen Pan, Qi Liu, Bai Long, Haiping Ma, Enhong Chen	To address the shortcomings above, in this paper, we propose a novel hybrid method based on feature learning of both Deep and Shallow Layers (DSL).
238	A Framework for Estimating Execution Times of IO Traces on SSDs	Yoonsuk Kang, Yong-Yeon Jo, Jaehyuk Cha, Wan D. Bae, Sang-Wook Kim	In this paper, we propose a framework of estimating the execution time of an application IO trace (i.e., a query IO trace) on a target SSD without its real execution.
239	Ranking Rich Mobile Verticals based on Clicks and Abandonment	Mami Kawasaki, Inho Kang, Tetsuya Sakai	In order to provide the right card types to the user for a given query, we propose a graph-based approach which extends a click-based automatic relevance estimation algorithm of Agrawal et al., by incorporating an abandonment-based preference rule. Using a real mobile query log from a commercial search engine, we constructed a data set containing 2,472 pairwise card type preferences covering 992 distinct queries, by hiring three independent assessors.
240	Semantic Rules for Machine Diagnostics: Execution and Management	Evgeny Kharlamov, Ognjen Savkoviý, Guohui Xiao, Rafael Penaloza, Gulnar Mehdi, Mikhail Roshchin, Ian Horrocks	In this paper we present how semantic technologies can enhance diagnostics.
241	Machine Learning based Performance Modeling of Flash SSDs	Jaehyung Kim, Jinuk Park, Sanghyun Park	In this paper, we examine the effectiveness of applying classification method using machine learning techniques to the I/O saturation estimation by using Linux kernel I/O statistics instead of the utilization measure that is currently used for HDDs.
242	A Robust Named-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information	Sunjae Kwon, Youngjoong Ko, Jungyun Seo	This paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can achieve a similar or better performance compared with the morphological-level NER systems.
243	IDAE: Imputation-boosted Denoising Autoencoder for Collaborative Filtering	Jae-woong Lee, Jongwuk Lee	In this paper, we propose a new CF model, namely the imputation-boosted denoising autoencoder (IDAE), for top-N recommendation.
244	Computing Betweenness Centrality in B-hypergraphs	Kwang Hee Lee, Myoung Ho Kim	In this paper every source node of a hyperedge in the shortest path p in a B-hypergraph is considered a participant of p.
245	Structural-fitting Word Vectors to Linguistic Ontology for Semantic Relatedness Measurement	Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Hsin-Hsi Chen	In this research, we propose a novel structural-fitting method that utilizes the linguistic ontology into vector space representations.
246	Alternating Pointwise-Pairwise Learning for Personalized Item Ranking	Yu Lei, Wenjie Li, Ziyu Lu, Miao Zhao	This paper proposes a novel joint learning method named alternating pointwise-pairwise learning (APPL) to improve ranking performance.
247	Deep Multi-Similarity Hashing for Multi-label Image Retrieval	Tong Li, Sheng Gao, Yajing Xu	In this paper, we proposed a framework named Deep Multi-Similarity Hashing (DMSH) method to learn semantic binary representations for multi-label image retrieval task.
248	Learning Graph-based Embedding For Time-Aware Product Recommendation	Yuqi Li, Weizheng Chen, Hongfei Yan	In this paper, we propose a novel Product Graph Embedding (PGE) model to investigate time-aware product recommendation by leveraging the network representation learning technique.
249	An Enhanced Topic Modeling Approach to Multiple Stance Identification	Junjie Lin, Wenji Mao, Yuhao Zhang	In this paper, we address the problem of recognizing distinct standpoints implied in textual data.
250	TICC: Transparent Inter-Column Compression for Column-Oriented Database Systems	Hao Liu, Yudian Ji, Jiang Xiao, Haoyu Tan, Qiong Luo, Lionel M. Ni	In this paper, we present TICC, an automatic data compression component that can transparently eliminate data redundancies across columns in column-oriented database systems.
251	Exploiting User Consuming Behavior for Effective Item Tagging	Shen Liu, Hongyan Liu	In this paper, we propose to leverage such information and introduce a probabilistic model called joint-tagging LDA to improve tagging accuracy.
252	SEQ: Example-based Query for Spatial Objects	Siqiang Luo, Jiafeng Hu, Reynold Cheng, Jing Yan, Ben Kao	In this paper, we propose the Spatial Exemplar Query (SEQ), which allows the user to input a result example over an interface inside the map service.
253	Truth Discovery by Claim and Source Embedding	Shanshan Lyu, Wentao Ouyang, Huawei Shen, Xueqi Cheng	Given these limitations, we propose a new, unsupervised model for truth discovery in this paper.
254	Automatic Catchphrase Identification from Legal Court Case Documents	Arpan Mandal, Kripabandhu Ghosh, Arindam Pal, Saptarshi Ghosh	In this work, we propose an unsupervised approach for extraction and ranking of catchphrases from court case documents, by focusing on noun phrases.
255	Learning Temporal Ambiguity in Web Search Queries	Behrooz Mansouri, Mohammad Sadegh Zahedi, Maseud Rahgozar, Farhad Oroumchian, Ricardo Campos	In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity.
256	Online Expectation-Maximization for Click Models	Ilya Markov, Alexey Borisov, Maarten de Rijke	To deal with outdated click information, we propose a variant of online EM called EM with Forgetting, which surpasses the performance of complete retraining while being as efficient as Online EM.
257	Task Embeddings: Learning Query Embeddings using Task Context	Rishabh Mehrotra, Emine Yilmaz	In this work, we hypothesize that task information provides better context for IR systems to learn from.
258	Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection	Zhao Meng, Lili Mou, Zhi Jin	We formulate text-based SCD as a matching problem of utterances before and after a certain decision point; we propose a hierarchical recurrent neural network (RNN) with static sentence-level attention.
259	Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes	Aditya Krishna Menon, Young Lee	In this paper, we show how such short term demand can be accurately modelled with an inhomogeneous Poisson process, using a neural network as the underlying intensity.
260	Analyzing Mathematical Content to Detect Academic Plagiarism	Norman Meuschke, Moritz Schubotz, Felix Hamborg, Tomas Skopal, Bela Gipp	This paper presents, to our knowledge, the first study on analyzing mathematical expressions to detect academic plagiarism. To facilitate future research on math-based plagiarism detection, we make our source code and data available.
261	Learning Entity Type Embeddings for Knowledge Graph Completion	Changsung Moon, Paul Jones, Nagiza F. Samatova	Inspired by recent work to build a contextual KG embedding model, we propose a novel approach to address the entity type prediction problem.
262	Identifying Top-K Influential Nodes in Networks	Sara Mumtaz, Xiaoyang Wang	With an attempt to deal with these challenges, our paper presents an approximate algorithm for BC maximization problem, which tries to find a set of nodes with largest BC.
263	Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation	Mir Tafseer Nayeem, Yllias Chali	This paper presents a first attempt towards finding an abstractive compression generation system for a set of related sentences which jointly models sentence fusion and paraphrasing using continuous vector representations.
264	J-REED: Joint Relation Extraction and Entity Disambiguation	Dat Ba Nguyen, Martin Theobald, Gerhard Weikum	This paper presents J-REED: a joint approach for entity disambiguation and relation extraction that is based on probabilistic graphical models.
265	Collaborative Topic Regression with Denoising AutoEncoder for Content and Community Co-Representation	Trong T. Nguyen, Hady W. Lauw	We seek to integrate both types of information, in addition to the adoption information, within a single integrated model.
266	Accurate Sentence Matching with Hybrid Siamese Networks	Massimo Nicosia, Alessandro Moschitti	In this paper, we learn sentence representations by means of a siamese network, which: (i) uses encoders that share parameters; and (ii) enables the comparison between two sentences in terms of their euclidean distance, by minimizing a contrastive loss.
267	Collaborative Sequence Prediction for Sequential Recommender	Shuzi Niu, Rongzhi Zhang	We propose to formulate the sequential recommendation problem as collaborative sequence prediction problem to take the dependency of users’ sequences into account.
268	Boolean Matrix Decomposition by Formal Concept Sampling	Petr Osicka, Martin Trnecka	We describe and experimentally evaluate a probabilistic algorithm for Boolean matrix decomposition problem.
269	Enhancing Knowledge Graph Completion By Embedding Correlations	Soumajit Pal, Jacopo Urbani	Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors.
270	Robust Heterogeneous Discriminative Analysis for Single Sample Per Person Face Recognition	Meng Pang, Yiu-ming Cheung, Binghui Wang, Risheng Liu	In this work, we propose a new patch-based method, namely Robust Heterogeneous Discriminative Analysis (RHDA), to tackle FR with SSPP.
271	Deep Neural Networks for News Recommendations	Keunchan Park, Jisoo Lee, Jaeho Choi	In this paper, we introduce deep neural network models to overcome these challenges.
272	TATHYA: A Multi-Classifier System for Detecting Check-Worthy Statements in Political Debates	Ayush Patwari, Dan Goldwasser, Saurabh Bagchi	We introduce a dataset of political debates from the 2016 US Presidential election campaign annotated using all major fact-checking media outlets and show that there is a need to model conversation context, debate dynamics and implicit world knowledge.
273	A Collaborative Ranking Model for Cross-Domain Recommendations	Dimitrios Rafailidis, Fabio Crestani	In this study, we propose a collaborative ranking model to generate cross-domain recommendations.
274	Combining Local and Global Word Embeddings for Microblog Stemming	Anurag Roy, Trishnendu Ghorai, Kripabandhu Ghosh, Saptarshi Ghosh	We propose an unsupervised, context-specific stemming algorithm for microblogs, based on both local and global word embeddings, which is capable of handling the informal, noisy vocabulary of microblogs.
275	An Improved Test Collection and Baselines for Bibliographic Citation Recommendation	Dwaipayan Roy	In this paper, we propose a way to modify this test collection to address these limitations.
276	A Way to Boost Semi-NMF for Document Clustering	Aghiles Salah, Melissa Ailem, Mohamed Nadif	Inspired by the recent success of neural word embedding models, e.g., word2vec, in learning high quality real valued vector representations of words, we propose to integrate a word embedding model into Semi-NMF.
277	Recipe Popularity Prediction with Deep Visual-Semantic Fusion	Satoshi Sanjo, Marie Katsurai	This paper presents a novel approach to predicting recipe popularity using deep visual-semantic fusion.
278	Revealing the Hidden Links in Content Networks: An Application to Event Discovery	Antonia Saravanou, Ioannis Katakis, George Valkanas, Vana Kalogeraki, Dimitrios Gunopulos	In this paper, we focus on how Content Networks can help us identify events effectively.
279	When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes	Arun V. Sathanur, Sutanay Choudhury, Cliff Joslyn, Sumit Purohit	In this work we tackle the problem of capturing the statistical dependence of the edge connectivity on the vertex labels and using the same distribution to regenerate property graphs of the same or expanded size in a scalable manner.
280	Integrating the Framing of Clinical Questions via PICO into the Retrieval of Medical Literature for Systematic Reviews	Harrisen Scells, Guido Zuccon, Bevan Koopman, Anthony Deacon, Leif Azzopardi, Shlomo Geva	The PICO framework is used extensively in the compilation of systematic reviews as the means of framing research questions.
281	pm-SCAN: an I/O Efficient Structural Clustering Algorithm for Large-scale Graphs	Jung Hyuk Seo, Myoung Ho Kim	We propose an I/O-efficient algorithm for structural clustering, pm-SCAN.
282	Knowledge Graph Embedding with Triple Context	Jun Shi, Huan Gao, Guilin Qi, Zhangquan Zhou	In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context.
283	Hybrid MemNet for Extractive Summarization	Abhishek Kumar Singh, Manish Gupta, Vasudeva Varma	To this end, we present a fully data-driven end-to-end deep network which we call as Hybrid MemNet for single document summarization task.
284	Denoising Clinical Notes for Medical Literature Retrieval with Convolutional Neural Model	Luca Soldaini, Andrew Yates, Nazli Goharian	In this work, we present a convolutional neural model aimed at improving clinical notes representation, making them suitable for document retrieval.
285	SIMD-Based Multiple Sets Intersection with Dual-Scale Search Algorithm	Xingshen Song, Yuexiang Yang, Xiaoyong Li	We present a flexible search algorithm which balances non-SIMD and SIMD comparisons in order to provide efficient and effective intersection.
286	Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval	Avikalp Srivastava, Madhav Datt	We propose a novel unsupervised model for semantic similarity based content retrieval, where we construct semantic flow graphs for each query, and introduce the concept of "soft seeding" in graph based semi-supervised learning (SSL) to convert this into an unsupervised model.
287	How Safe is Your (Taxi) Driver?	Rade Stanojevic	In this paper we discuss a methodology for studying driver risk assessment using a public dataset of 173M taxi rides in NYC with over 40K drivers.
288	Sentence Retrieval with Sentiment-specific Topical Anchoring for Review Summarization	Jiaxing Tan, Alexander Kotov, Rojiar Pir Mohammadiani, Yumei Huo	We propose Topic Anchoring-based Review Summarization (TARS), a two-step extractive summarization method, which creates review summaries from the sentences that represent the most important aspects of a review.
289	Visualizing Deep Neural Networks with Interaction of Super-pixels	Shixin Tian, Ying Cai	In the existing works, these units are largely considered independently, thus limiting the performance of visualization.
290	Collecting Non-Geotagged Local Tweets via Bandit Algorithms	Saki Ueda, Yuto Yamaguchi, Hiroyuki Kitagawa	In this paper, we propose a framework that incrementally finds such users and continuously collects tweets from them.
291	A Temporal Attentional Model for Rumor Stance Classification	Amir Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, Daniel Lowd	In this work, we analyze Twitter users’ stance toward a rumorous tweet, in which users could support, deny, query, or comment upon the rumor.
292	Improving the Gain of Visual Perceptual Behaviour on Topic Modeling for Text Recommendation	Cheng Wang, Yujuan Fang, Zheng Tan, Yuan He	In this paper, we mainly aim at improving the gain of visual perceptual behaviour for text recommendation, by integrating the objective contents with subjective visual perceptual behaviours.
293	Semantic Annotation for Places in LBSN through Graph Embedding	Yan Wang, Zongxu Qin, Jun Pang, Yang Zhang, Jin Xin	Our underlying idea is that a place can be considered as a representative of all its visitors.
294	A Study of Feature Construction for Text-based Forecasting of Time Series Variables	Yiren Wang, Dominic Seyler, Shubhra Kanti Karmaker Santu, ChengXiang Zhai	In this paper, we study how to construct effective additional features based on related text data for time series forecasting.
295	Using Knowledge Graphs to Explain Entity Co-occurrence in Twitter	Yiwei Wang, Mark James Carman, Yuan-Fang Li	In this paper we investigate whether these two sources of information can be used to complement and explain one another.
296	Integrating Side Information for Boosting Machine Comprehension	Yutong Wang, Yixin Xu, Min Yang, Zhou Zhao, Jun Xiao, Yueting Zhuang	In this paper, we consider integrating side information to improve machine comprehension on answering cloze-style questions more precisely.
297	Unsupervised Feature Selection with Heterogeneous Side Information	Xiaokai Wei, Bokai Cao, Philip S. Yu	In this paper, we propose a new feature selection method, SideFS, to exploit such rich side information.
298	An Empirical Study of Community Overlap: Ground-truth, Algorithmic Solutions, and Implications	Joyce Jiyoung Whang	In this paper, we investigate the properties of the nodes and the edges placed within the overlapped regions between the communities using the ground-truth communities as well as algorithmic communities derived from the state-of-the-art overlapping community detection methods.
299	Non-Exhaustive, Overlapping Co-Clustering	Joyce Jiyoung Whang, Inderjit S. Dhillon	To solve this problem, we propose an intuitive objective function, and develop an efficient iterative algorithm which we call the NEO-CC algorithm.
300	Simulating Zero-Resource Spoken Term Discovery	Jerome White, Douglas W. Oard	This paper describes a text-based simulation of a zero-resource spoken term discovery system that allows any information retrieval test collection to be used as a basis for early development of information retrieval techniques.
301	Algorithmic Bias: Do Good Systems Make Relevant Documents More Retrievable?	Colin Wilkie, Leif Azzopardi	This work evaluates the varying degrees of bias present in the groups of relevant and non-relevant documents for topics.
302	Session-aware Information Embedding for E-commerce Product Recommendation	Chen Wu, Ming Yan	In this paper, we propose a list-wise deep neural network based architecture to model the limited user behaviors within each session.
303	Conflict of Interest Declaration and Detection System in Heterogeneous Networks	Siyuan Wu, Leong Hou U, Sourav S. Bhowmick, Wolfgang Gatterbauer	In this work, we study a graphical declaration system that visualizes the relationships of authors and reviewers based on a heterogeneous co-authorship network.
304	Common-Specific Multimodal Learning for Deep Belief Network	Changsheng Xiang, Xiaoming Jin	This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem.
305	JointSem: Combining Query Entity Linking and Entity based Document Ranking	Chenyan Xiong, Zhengzhong Liu, Jamie Callan, Eduard Hovy	This work presents JointSem, a joint semantic ranking system that combines query entity linking and entity-based document ranking.
306	Learning to Rank with Query-level Semi-supervised Autoencoders	Bo Xu, Hongfei Lin, Yuan Lin, Kan Xu	To enrich the feature space for learning to rank, we introduce supervision into the loss functions of autoencoders.
307	MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis	Nan Xu, Wenji Mao	In this paper, we propose a deep semantic network, namely MultiSentiNet, for multimodal sentiment analysis.
308	Attentive Graph-based Recursive Neural Network for Collective Vertex Classification	Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu	In this paper, we propose an Attentive Graph-based Recursive Neural Network (AGRNN), which exerts attention on neural network to make our model focus on vertices with more relevant semantic information.
309	Bayesian Heteroscedastic Matrix Factorization for Conversion Rate Prediction	Hongxia Yang	We focus on matrix CVR predictions in this paper but the proposed BHMF can be naturally extended and applied to higher dimensional tensors.
310	SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories	Di Yao, Chao Zhang, Jianhui Huang, Jingping Bi	We propose a method named semantics-enriched recurrent model (SERM).
311	Low-Rank Matrix Completion over Finite Abelian Group Algebras for Context-Aware Recommendation	Chia-An Yu, Tak-Shing Chan, Yi-Hsuan Yang	In this paper, we address this by using matrices over finite abelian group algebra (AGA) to model context-aware interactions between users and items.
312	Spectrum-based Deep Neural Networks for Fraud Detection	Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu	In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data.
313	RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation	Yu Zhang, Wei Wei, Binxuan Huang, Kathleen M. Carley, Yan Zhang	In this paper, we aim to tackle these two problems.
314	Missing Value Learning	Zhi-Lin Zhao, Chang-Dong Wang, Kun-Yu Lin, Jian-Huang Lai	In this paper, we focus on learning from the known values to learn missing value as close as possible to the true one.
315	Local Ensemble across Multiple Sources for Collaborative Filtering	Jing Zheng, Fuzhen Zhuang, Chuan Shi	In this paper, we propose a novel LO cal EN semble framework across multiple source domains for collaborative filtering (called LOEN for short), where weights of multiple sources for each missing rating in the target domain are determined according to their corresponding local structures.
316	Cluster-level Emotion Pattern Matching for Cross-Domain Social Emotion Classification	Endong Zhu, Yanghui Rao, Haoran Xie, Yuwei Liu, Jian Yin, Fu Lee Wang	This paper proposes a novel framework, which uses the emotion distribution of training documents at the cluster level, to alleviate the aforementioned issue.
317	Knowledge-based Question Answering by Jointly Generating, Copying and Paraphrasing	Shuguang Zhu, Xiang Cheng, Sen Su, Shuang Lang	In this paper, we focus on simple questions, which ask about only a subject and relation in the knowledge base.
318	PODIUM: Procuring Opinions from Diverse Users in a Multi-Dimensional World	Yael Amsterdamer, Oded Goldreich	We present PODIUM, a tool for opinion procurement that accounts for complex user profiles and enables customizable user selection.
319	VizQ: A System for Scalable Processing of Visibility Queries in 3D Spatial Databases	Arif Arman, Mohammed Eunus Ali, Farhana Murtaza Choudhury, Kaysar Abdullah	In this demonstration, we present VizQ, an efficient, scalable, and interactive system to process and visualize a comprehensive collection of novel visibility queries in the presence of obstacles in 3D space.
320	CoreDB: a Data Lake Service	Amin Beheshti, Boualem Benatallah, Reza Nouri, Van Munin Chhieng, HuangTao Xiong, Xu Zhao	To address the above mentioned challenges, we present CoreDB – an open source data lake service – which offers researchers and developers a single REST API to organize, index and query their data and metadata.
321	SimMeme: Semantic-Based Meme Search	Maya Ekron, Tova Milo, Brit Youngmann	In this work, we focus on a particular class of images that convey semantic meaning beyond the visual appearance, and whose search presents particular challenges.
322	SummIt: A Tool for Extractive Summarization, Discovery and Analysis	Guy Feigenblat, Odellia Boni, Haggai Roitman, David Konopnicki	We propose to demonstrate SummIt — a tool for extractive summarization, discovery and analysis.
323	Rapid Analysis of Network Connectivity	Scott Freitas, Hanghang Tong, Nan Cao, Yinglong Xia	To facilitate this process we utilize: (1) multi-threaded algorithm variations, (2) network re-use for subsequent queries and (3) a novel algorithm, Key Neighboring Vertices (KNV), to reduce the network search space.
324	HyPerInsight: Data Exploration Deep Inside HyPer	Nina Hubig, Linnea Passing, Maximilian E. Schüle, Dimitri Vorona, Alfons Kemper, Thomas Neumann	We propose to extend HyPer, a main memory database system to a uniform data agent platform following the one system fits all approach for solving a wide variety of data analysis problems.
325	Interactive System for Reasoning about Document Age	Adam Jatowt, Ricardo Campos	In this paper, we demonstrate an interactive system for estimating the age of documents.
326	SemFacet: Making Hard Faceted Search Easier	Evgeny Kharlamov, Luca Giacomelli, Evgeny Sherkhonov, Bernardo Cuenca Grau, Egor V. Kostylev, Ian Horrocks	In this demo we present (an extension of) our faceted search system SemFacet and focus on features that address the information overload: ranking, aggregation, and reachability.
327	Metacrate: Organize and Analyze Millions of Data Profiles	Sebastian Kruse, David Hahn, Marius Walter, Felix Naumann	In particular, we (i) propose a logical and a physical data model to store all kinds of data profiles in a scalable fashion; (ii) describe an analytics layer to query, integrate, and analyze the profiles efficiently; and (iii) implement on top a library of established algorithms to serve use cases, such as schema discovery, database refactoring, and data cleaning.
328	SemVis: Semantic Visualization for Interactive Topical Analysis	Tuan M. V. Le, Hady W. Lauw	Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially.
329	Exploring the Veracity of Online Claims with BackDrop	Julien Leblay, Weiling Chen, Steven Lynden	We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context.
330	AliMe Assist : An Intelligent Assistant for Creating an Innovative E-commerce Experience	Feng-Lin Li, Minghui Qiu, Haiqing Chen, Xiongwei Wang, Xing Gao, Jun Huang, Juwei Ren, Zhongzhou Zhao, Weipeng Zhao, Lei Wang, Guwei Jin, Wei Chu	In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.
331	Public Transportation Mode Detection from Cellular Data	Guanyao Li, Chun-Jie Chen, Sheng-Yun Huang, Ai-Jou Chou, Xiaochuan Gou, Wen-Chih Peng, Chih-Wei Yi	In this paper, we refer to some external data sources (e.g., the bus routing networks) to identify transportation modes.
332	Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data	Mengxiong Liu, Zhengchao Liu, Chao Zhang, Keyang Zhang, Quan Yuan, Tim Hanratty, Jiawei Han	We present Urbanity, a novel system that leverages geo-tagged social media streams for modeling urban dynamics.
333	SemDia: Semantic Rule-Based Equipment Diagnostics Tool	Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savković, Guohui Xiao, Elem Güzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin, Thomas Runkler	In this demo we present how semantic technologies can enhance diagnostics.
334	TaCLe: Learning Constraints in Tabular Data	Sergey Paramonov, Samuel Kolb, Tias Guns, Luc De Raedt	To address this issue we have introduced the TaCLe system (Tabular Constraint Learner).
335	An Interactive Framework for Video Surveillance Event Detection and Modeling	Fabio Persia, Fabio Bettini, Sven Helmer	We present a framework for high-level event detection in video streams based on a novel temporal extension of relational algebra.
336	Storyfinder: Personalized Knowledge Base Construction and Management by Browsing the Web	Steffen Remus, Manuel Kaufmann, Kathrin Ballweg, Tatiana von Landesberger, Chris Biemann	This paper presents Storyfinder, an application which consists of a browser plugin and a web server backend with the goal to highlight and manage the information contained in web pages by combining techniques from natural language processing and visual analytics.
337	IMaxer: A Unified System for Evaluating Influence Maximization in Location-based Social Networks	Muhammad Aamir Saleem, Rohit Kumar, Toon Calders, Xike Xie, Torben Bach Pedersen	In this demonstration, we present a unified system IMaxer that both provides a complete pipeline of state-of-the-art and novel models and algorithms for influence maximization (IM) as well as allows to evaluate and compare IM techniques for a particular scenario.
338	StreamingCube: A Unified Framework for Stream Processing and OLAP Analysis	Salman Ahmed Shaikh, Hiroyuki Kitagawa	To this end, we present StreamingCube, a unified framework for data stream processing and its interactive OLAP analysis.
339	Product Exploration based on Latent Visual Attributes	Tomáš Skopal, Ladislav Peška, Gregor Kovalčík, Tomáš Grosup, Jakub Lokoč	In this demo paper, we present a prototype web application of a product search engine of a fashion e-shop.
340	Hierarchical Module Classification in Mixed-initiative Conversational Agent System	Sia Xin Yun Suzanna, Li Lianjie Anthony	We address these challenges with a mixed-initiative model that controls conversational logic through hierarchical classification.
341	Blockchain-based Data Management and Analytics for Micro-insurance Applications	Hoang Tam Vo, Lenin Mehedy, Mukesh Mohania, Ermyas Abebe	In this paper, we demonstrate a blockchain-based solution for transparently managing and analyzing data in a pay-as-you-go car insurance application.
342	CleanCloud: Cleaning Big Data on Cloud	Hongzhi Wang, Xiaoou Ding, Xiangying Chen, Jianzhong Li, Hong Gao	We describe CleanCloud, a system for cleaning big data based on Map-Reduce paradigm in cloud.
343	Interactive Analytics System for Exploring Outliers	Mingrui Wei, Lei Cao, Chris Cormier, Hui Zheng, Elke A. Rundensteiner	We demonstrate ONION’s capabilities with urban planning applications use cases on the Open Street Maps dataset.
344	Query and Animate Multi-attribute Trajectory Data	Jianqiu Xu, Ralf Hartmut Güting	In this demo, we provide the motivation scenario and introduce a system that is developed to integrate standard trajectories (a sequence of timestamped locations) and attributes into one unified framework.
345	ClaimVerif: A Real-time Claim Verification System Using the Web and Fact Databases	Shi Zhi, Yicheng Sun, Jiayi Liu, Chao Zhang, Jiawei Han	We build ClaimVerif, a claim verification system that not only provides credibility assessment for any user-given query claim, but also rationales the assessment results with supporting evidences.
346	POOLSIDE: An Online Probabilistic Knowledge Base for Shopping Decision Support	Ping Zhong, Zhanhuai Li, Qun Chen, Yanyan Wang, Lianping Wang, Murtadha HM Ahmed, Fengfeng Fan	We present POOLSIDE, an online PrObabilistic knOwLedge base for ShoppIng DEcision support, that provides with the on-target recommendation service based on explicit user requirement.
347	Overview of the 4th HistoInformatics Workshop	Mohammed Hasanuzzaman, Gaël Dias, Adam Jatowt, Marten Düring, Antal van den Bosch	The HistoInformatics workshop series is focused on the challenges and opportunities of data-driven humanities and brings together scientists and scholars at the forefront of this emerging field, at the interface between History, Anthropology, Archaeology, Computer Science and associated disciplines as well as the cultural heritage sector.
348	IDM 2017: Workshop on Interpretable Data Mining — Bridging the Gap between Shallow and Deep Models	Xia Hu, Shuiwang Ji	This workshop is about interpreting the prediction mechanisms or results of the complex computational models for data mining by taking advantage of simple models which are easier to understand.
349	SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities	Manjira Sinha, Xiangnan He, Alessandro Bozzon, Sandya Mannarswamy, Pradeep Murukannaiah, Tridib Mukherjee	The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.
350	Additional Workshops Co-located with CIKM 2017	Marianne Winslett	Additional Workshops Co-located with CIKM 2017