Paper Digest: SIGIR 2014 Highlights

July 28, 2014June 26, 2020 admin

SIGIR (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval) is one of the top information retrieval conferences in the world.

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: SIGIR 2014 Papers

	Title	Authors	Highlight
1	Putting searchers into search	Susan T. Dumais	To address these challenges we need to extend our evaluation methods to handle the diversity of searchers, tasks, and interactivity that characterize information systems today.
2	Modelling interaction with economic models of search	Leif Azzopardi	In this paper, we extend the recently proposed search economic theory to make the model more realistic.
3	Query-performance prediction: setting the expectations straight	Fiana Raiber, Oren Kurland	Focusing on this specific prediction task, namely query ranking by presumed effectiveness, we present a novel learning-to-rank-based approach that uses Markov Random Fields.
4	Hypothesis testing for the risk-sensitive evaluation of retrieval systems	B. Taner Dinçer, Craig Macdonald, Iadh Ounis	Hence, we introduce a risk-reward tradeoff measure TRisk that generalises the existing URisk measure (as used in the TREC 2013 Web track’s risk-sensitive task) while being theoretically grounded in statistical hypothesis testing and easily interpretable.
5	Temporal feedback for tweet search with non-parametric density estimation	Miles Efron, Jimmy Lin, Jiyin He, Arjen de Vries	Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model.
6	Fine-grained location extraction from tweets with temporal awareness	Chenliang Li, Aixin Sun	In this paper, we are interested in extracting fine-grained locations mentioned in tweets with temporal awareness.
7	Collaborative personalized Twitter search with topic-language models	Jan Vosecky, Kenneth Wai-Ting Leung, Wilfred Ng	In this paper, we therefore propose a novel framework for Collaborative Personalized Twitter Search.
8	Gaussian process factorization machines for context-aware recommendations	Trung V. Nguyen, Alexandros Karatzoglou, Linas Baltrunas	To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes.
9	Addressing cold start in recommender systems: a semi-supervised co-training algorithm	Mi Zhang, Jie Tang, Xuchen Zhang, Xiangyang Xue	In this paper we tackle the cold-start problem by proposing a context-aware semi-supervised co-training method named CSEL.
10	Explicit factor models for explainable recommendation based on phrase-level sentiment analysis	Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, Shaoping Ma	In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy.
11	Context-aware web search abandonment prediction	Yang Song, Xiaolin Shi, Ryen White, Ahmed Hassan Awadallah	We propose more advanced methods for modeling and predicting abandonment rationales using contextual information from user search sessions by analyzing search engine logs, and discover dependencies between abandoned queries and user behaviors.
12	Impact of response latency on user behavior in web search	Ioannis Arapakis, Xiao Bai, B. Barla Cambazoglu	In order to fill this gap, we conduct two separate studies aiming to reveal how response latency affects the user behavior in web search.
13	Towards better measurement of attention and satisfaction in mobile search	Dmitry Lagun, Chih-Hung Hsieh, Dale Webster, Vidhya Navalpakkam	In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks.
14	Modeling action-level satisfaction for search task satisfaction prediction	Hongning Wang, Yang Song, Ming-Wei Chang, Xiaodong He, Ahmed Hassan, Ryen W. White	To do this, we develop a latent structural learning method, whereby rich structured features and dependency relations unique to search satisfaction prediction are explored.
15	Circumlocution in diagnostic medical queries	Isabelle Stanton, Samuel Ieong, Nina Mishra	Given a free-form colloquial health search query, our objective is to find the underlying professional medical term.
16	Interactions between health searchers and search engines	Georg P. Schoenherr, Ryen W. White	In this paper, we investigate potential strate- gies to mine queries and searcher histories for clues that could help search engines choose the most appropriate infor- mation to present in response to exploratory medical queries.
17	Evaluation of machine-learning protocols for technology-assisted review in electronic discovery	Gordon V. Cormack, Maura R. Grossman	Abstract Using a novel evaluation toolkit that simulates a human reviewer in the loop, we compare the effectiveness of three machine-learning protocols for technology-assisted review as used in document review for discovery in legal proceedings.
18	ReQ-ReC: high recall retrieval with query pooling and interactive classification	Cheng Li, Yue Wang, Paul Resnick, Qiaozhu Mei	We describe and demonstrate the effectiveness of ReQ-ReC (ReQuery-ReClassify), a double-loop retrieval system that combines iterative expansion of a query set with iterative refinements of a classifier.
19	Supervised hashing with latent factor models	Peichao Zhang, Wei Zhang, Wu-Jun Li, Minyi Guo	In this paper, we propose a novel supervised hashing method, called latent factor hashing(LFH), to learn similarity-preserving binary codes based on latent factor models.
20	Preference preserving hashing for efficient recommendation	Zhiwei Zhang, Qifan Wang, Lingyun Ruan, Luo Si	In this paper, a novel hashing algorithm, named Preference Preserving Hashing (PPH), is proposed to speed up recommendation.
21	Load balancing for partition-based similarity search	Xun Tang, Maha Alabduljalil, Xin Jin, Tao Yang	This paper presents a two-stage heuristic algorithm to improve the load balance and shorten the overall processing time.
22	Estimating global statistics for unstructured P2P search in the presence of adversarial peers	Sami Richardson, Ingemar J. Cox	As a defense, we propose a simple modification to the extension, and show global statistics estimation is viable even when up to 40% of peers are adversarial.
23	Hierarchical multi-label classification of social text streams	Zhaochun Ren, Maria-Hendrike Peetz, Shangsong Liang, Willemijn van Dolen, Maarten de Rijke	In this paper we focus on hierarchical multi-label classification of social text streams.
24	An adaptive teleportation random walk model for learning social tag relevance	Xiaofei Zhu, Wolfgang Nejdl, Mihai Georgescu	In this paper, we cast the social tag relevance learning problem as an adaptive teleportation random walk process on the voting graph.
25	Predicting the popularity of web 2.0 items based on user comments	Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama	To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts.
26	Recommending social media content to community owners	Inbal Ronen, Ido Guy, Elad Kravi, Maya Barnea	We compared seven different methods for generating recommendations, including content-based, member-based, and hybridization of the two.
27	Predictive parallelization: taming tail latencies in web search	Myeongjae Jeon, Saehoon Kim, Seung-won Hwang, Yuxiong He, Sameh Elnikety, Alan L. Cox, Scott Rixner	Motivated by these observations, we propose a predictive parallelization framework with two parts: (1) predicting long-running queries, and (2) selectively parallelizing them.
28	Skewed partial bitvectors for list intersection	Andrew Kane, Frank Wm. Tompa	To refute this belief, we propose several approaches to improve the performance of ranking-based search systems using bitvectors, and leave their verification for future work.
29	Partitioned Elias-Fano indexes	Giuseppe Ottaviano, Rossano Venturini	In this paper we describe a new representation based on partitioning the list into chunks and encoding both the chunks and their endpoints with Elias-Fano, hence forming a two-level data structure.
30	Principled dictionary pruning for low-memory corpus compression	Jiancong Tong, Anthony Wirth, Justin Zobel	We develop a formal model of our approach, based on generating an optimal dictionary for a given collection within a memory bound.
31	Learning for search result diversification	Yadong Zhu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Shuzi Niu	In this paper, we address search result diversification as a learning problem, and introduce a novel relational learning-to-rank approach to formulate the task.
32	Fusion helps diversification	Shangsong Liang, Zhaochun Ren, Maarten de Rijke	We adopt a different perspective on the problem, based on data fusion.
33	Utilizing relevance feedback in fusion-based retrieval	Ella Rabinovich, Ofri Rom, Oren Kurland	Accordingly, we devise methods that utilize the feedback while exploiting the special characteristics of the fusion setting.
34	A simple term frequency transformation model for effective pseudo relevance feedback	Zheng Ye, Jimmy Xiangji Huang	In this paper, we propose a simple and heuristic, but effective model, in which three term frequency transformation techniques are integrated to capture the saliency of a candidate term associated with the original query terms in the feedback documents.
35	Seeking simplicity in search user interfaces	Marti A. Hearst	In this talk I will give examples of such successes in the information-intensive interface design space, and attempt to provide stimulating ideas for future research directions.
36	Who is the barbecue king of texas?: a geo-spatial approach to finding local experts on twitter	Zhiyuan Cheng, James Caverlee, Himanshu Barthwal, Vandana Bachani	Hence in this paper, we propose a geo-spatial-driven approach for identifying local experts that leverages the fine-grained GPS coordinates of millions of Twitter users.
37	Your neighbors affect your ratings: on geographical neighborhood influence to rating prediction	Longke Hu, Aixin Sun, Yong Liu	Using the business review data from Yelp, in this paper, we study business rating prediction.
38	Processing spatial keyword query as a top-k aggregation query	Dongxiang Zhang, Chee-Yong Chan, Kian-Lee Tan	In this paper, we propose a new approach that is based on modeling the problem as a top-k aggregation problem which enables the design of a scalable and efficient solution that is based on the ubiquitous inverted list index.
39	Entity query feature expansion using knowledge base links	Jeffrey Dalton, Laura Dietz, James Allan	In this paper, we propose a new technique, called entity query feature expansion (EQFE) which enriches the query with features from entities and their links to knowledge bases, including structured attributes and text.
40	QUADS: question answering for decision support	Zi Yang, Ying Li, James Cai, Eric Nyberg	In this paper, we propose a novel decision representation which allows decision makers to formulate and organize natural language questions or assertions into an analytic hierarchy, which can be evaluated as part of an ad hoc decision process or as a documented, repeatable analytic process.
41	Topic labeled text classification: a weakly supervised approach	Swapnil Hingmire, Sutanu Chakraborti	In this paper, we propose a weakly supervised text classification algorithm based on the labeling of Latent Dirichlet Allocation (LDA) topics.
42	Discriminative coupled dictionary hashing for fast cross-media retrieval	Zhou Yu, Fei Wu, Yi Yang, Qi Tian, Jiebo Luo, Yueting Zhuang	We propose a discriminative coupled dictionary hashing (DCDH) method in this paper.
43	Active hashing with joint data example and tag selection	Qifan Wang, Luo Si, Zhiwei Zhang, Ning Zhang	This paper proposes a novel active hashing approach, Active Hashing with Joint Data Example and Tag Selection (AH-JDETS), which actively selects the most informative data examples and tags in a joint manner for hashing function learning.
44	Latent semantic sparse hashing for cross-modal similarity search	Jile Zhou, Guiguang Ding, Yuchen Guo	To address these challenges, in this paper, we propose a novel Latent Semantic Sparse Hashing (LSSH) to perform cross-modal similarity search by employing Sparse Coding and Matrix Factorization.
45	Predicting term-relevance from brain signals	Manuel J.A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé, Ilkka Kosunen, Oswald Barral, Niklas Ravaja, Giulio Jacucci, Samuel Kaski	As an application of TRPB we demonstrate a high-precision variant of the classifier that constructs sets of relevant terms for a given unknown topic of interest.
46	Multidimensional relevance modeling via psychometrics and crowdsourcing	Yinglong Zhang, Jin Zhang, Matthew Lease, Jacek Gwizdka	In this work, we identify and address these limitations, scale their methodology via crowdsourcing, and describe quality control methods from psychometrics which stand to benefit crowdsourcing IR studies in general.
47	Learning user reformulation behavior for query auto-completion	Jyun-Yu Jiang, Yen-Yu Ke, Pao-Yu Chien, Pu-Jen Cheng	The purpose of this paper is to investigate the feasibility of exploiting the context to learn user reformulation behavior for boosting prediction performance.
48	A two-dimensional click model for query auto-completion	Yanen Li, Anlei Dong, Hongning Wang, Hongbo Deng, Yi Chang, ChengXiang Zhai	In order to better explain them, we pro- pose a novel two-dimensional click model for modeling the QAC process with emphasis on these behaviors.
49	On measuring social friend interest similarities in recommender systems	Hao Ma	In this paper, aiming at providing fundamental support to the research of social recommendation problem, we conduct an in-depth analysis on the correlations between social friend relations and user interest similarities.
50	IMRank: influence maximization via finding self-consistent ranking	Suqi Cheng, Huawei Shen, Junming Huang, Wei Chen, Xueqi Cheng	Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1) Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2) Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.
51	User-driven system-mediated collaborative information retrieval	Laure Soulier, Chirag Shah, Lynda Tamine	In this paper, we propose to combine both of these approaches by a role mining methodology that learns from users’ actions about the retrieval strategy they adapt.
52	SearchPanel: framing complex search needs	Pernilla Qvarfordt, Simon Tretter, Gene Golovchinsky, Tony Dunnigan	This extension combines document and process metadata into an interactive representation of the retrieved documents that can be used for sense-making, navigation, and re-finding documents.
53	Cohort modeling for enhanced personalized search	Jinyun Yan, Wei Chu, Ryen W. White	In this paper we describe a characterization and evaluation of the use of such cohort modeling to enhance search personalization.
54	Characterizing multi-click search behavior and the risks and opportunities of changing results during use	Chia-Jung Lee, Jaime Teevan, Sebastian de la Chica	Using large scale query log analysis, we characterize what people do when they return to a result page after having visited an initial result.
55	The data revolution: how companies are transforming with big data	Hugh E. Williams	In this talk, Hugh Williams shares stories about data and how it is used to build Internet products, and explains why he believes data will transform businesses as we know them.
56	Learning similarity functions for topic detection in online reputation monitoring	Damiano Spina, Julio Gonzalo, Enrique Amigó	We focus on a solution for the problem that (i) learns a pairwise tweet similarity function from previously annotated data, using all kinds of content-based and Twitter-based features; (ii) applies a clustering algorithm on the previously learned similarity function.
57	Predicting trending messages and diffusion participants in microblogging network	Jingwen Bian, Yang Yang, Tat-Seng Chua	In this work, we define three types of influences, namely, interest-oriented influence, social-oriented influence, and epidemic-oriented influence, that will affect a user’s decision on whether to perform a diffusion action.
58	Leveraging knowledge across media for spammer detection in microblogging	Xia Hu, Jiliang Tang, Huan Liu	Inspired by the findings, we present an optimization formulation that enables the design of spammer detection in microblogging using knowledge from external media.
59	Using information scent and need for cognition to understand online search behavior	Wan-Ching Wu, Diane Kelly, Avneesh Sud	The purpose of this study is to investigate the extent to which two theories, Information Scent and Need for Cognition, explain people’s search behaviors when interacting with search engine results pages (SERPs).
60	Discrimination between tasks with user activity patterns during information search	Michael J. Cole, Chathra Hendahewa, Nicholas J. Belkin, Chirag Shah	We model sequences of interactions with search result and content pages during information search sessions.
61	Investigating users’ query formulations for cognitive search intents	Makoto P. Kato, Takehiro Yamamoto, Hiroaki Ohshima, Katsumi Tanaka	This study investigated query formulations by users with {\it Cognitive Search Intents} (CSIs), which are users’ needs for the cognitive characteristics of documents to be retrieved, {\em e.g. comprehensibility, subjectivity, and concreteness.
62	Win-win search: dual-agent stochastic game in session search	Jiyun Luo, Sicong Zhang, Hui Yang	We thus propose to model session search as a dual-agent stochastic game: the user agent and the search engine agent work together to jointly maximize their long term rewards.
63	Injecting user models and time into precision via Markov chains	Marco Ferrante, Nicola Ferro, Maria Maistro	We propose a family of new evaluation measures, called Markov Precision (MP), which exploits continuous-time and discrete-time Markov chains in order to inject user models into precision.
64	Searching, browsing, and clicking in a search session: changes in user behavior by task and over time	Jiepu Jiang, Daqing He, James Allan	In this paper, we characterize and compare user behavior in relatively long search sessions (10 minutes; about 5 queries) for search tasks of four different types.
65	Coarse-to-fine review selection via supervised joint aspect and sentiment model	Zhen Hai, Gao Cong, Kuiyu Chang, Wenting Liu, Peng Cheng	We propose a novel supervised joint aspect and sentiment model (SJASM), which is a probabilistic topic modeling framework that jointly discovers aspects and sentiments guided by a review helpfulness metric.
66	Cross-domain and cross-category emotion tagging for comments of online news	Ying Zhang, Ning Zhang, Luo Si, Yanshan Lu, Qifan Wang, Xiaojie Yuan	This paper proposes a novel framework to transfer knowledge across different news domains.
67	Economically-efficient sentiment stream analysis	Roberto Lourenco Jr., Adriano Veloso, Adriano Pereira, Wagner Meira Jr., Renato Ferreira, Srinivasan Parthasarathy	In this paper we address these challenges by proposing algorithms that select relevant training instances at each time step, so that training sets are kept small while providing to the classifier the capabilities to suit itself to, and to recover itself from, different types of sentiment drifts.
68	New and improved: modeling versions to improve app recommendation	Jovian Lin, Kazunari Sugiyama, Min-Yen Kan, Tat-Seng Chua	We present a novel framework that incorporates features distilled from version descriptions into app recommendation.
69	Bundle recommendation in ecommerce	Tao Zhu, Patrick Harrington, Junjun Li, Lei Tang	In this paper, we introduce a novel problem called the Bundle Recommendation Problem (BRP).
70	Does product recommendation meet its waterloo in unexplored categories?: no, price comes to help	Jia Chen, Qin Jin, Shiwan Zhao, Shenghua Bao, Li Zhang, Zhong Su, Yong Yu	In this paper, we investigate the challenge problem of product recommendation in unexplored categories and discover that the price, a factor transferrable across categories, can improve the recommendation performance significantly.
71	Query expansion for mixed-script information retrieval	Parth Gupta, Kalika Bali, Rafael E. Banchs, Monojit Choudhury, Paolo Rosso	In this paper, we formally introduce the concept of Mixed-Script IR, and through analysis of the query logs of Bing search engine, estimate the prevalence and thereby establish the importance of this problem.
72	Retrieval of similar chess positions	Debasis Ganguly, Johannes Leveling, Gareth J.F. Jones	We address the problem of retrieving chess game positions similar to a given query position from a collection of archived chess games.
73	A mathematics retrieval system for formulae in layout presentations	Xiaoyan Lin, Liangcai Gao, Xuan Hu, Zhi Tang, Yingnan Xiao, Xiaozhong Liu	This paper proposes an innovative mathematics retrieval system along with the novel algorithms, which enables efficient formula index and retrieval from both webpages and PDF documents.
74	The knowing camera 2: recognizing and annotating places-of-interest in smartphone photos	Pai Peng, Lidan Shou, Ke Chen, Gang Chen, Sai Wu	We propose a`"Spatial+Visual" (S+V) framework which consists of a probabilistic field-of-view model in the spatial phase and sparse coding similarity metric in the visual phase to recognize phone-captured POIs.
75	Click-through-based cross-view learning for image search	Yingwei Pan, Ting Yao, Tao Mei, Houqiang Li, Chong-Wah Ngo, Yong Rui	We demonstrate in this paper that the above two fundamental challenges can be mitigated by jointly exploring the cross-view learning and the use of click-through data.
76	Learning to personalize trending image search suggestion	Chun-Che Wu, Tao Mei, Winston H. Hsu, Yong Rui	In this paper, we move one step forward to investigate personalized suggestion of trending image searches according to users’ search behaviors.
77	PRISM: concept-preserving social image search results summarization	Boon-Siew Seah, Sourav S. Bhowmick, Aixin Sun	In this paper, we present a novel concept-preserving image search results summarization algorithm named Prism.
78	Time-critical search	Nina Mishra, Ryen W. White, Samuel Ieong, Eric Horvitz	We study time-critical search, where users have urgent information needs in the context of an acute problem.
79	Learning temporal-dependent ranking models	Miguel Costa, Francisco Couto, Mário Silva	Based on the assumption that closer periods are more likely to hold similar web characteristics, our framework learns multiple models simultaneously, each tuned for a specific period.
80	Web page segmentation with structured prediction and its application in web page classification	Lidong Bing, Rui Guo, Wai Lam, Zheng-Yu Niu, Haifeng Wang	We propose a framework which can perform Web page segmentation with a structured prediction approach.
81	Query log driven web search results clustering	Jose G. Moreno, Gaël Dias, Guillaume Cleuziou	Following this trend, we present a new algorithm called Dual C-Means, which provides a theoretical background for clustering in different representation spaces.
82	CTSUM: extracting more certain summaries for news articles	Xiaojun Wan, Jianmin Zhang	In this paper, we propose a novel system called CTSUM to incorporate the new factor of information certainty into the summarization task.
83	Continuous word embeddings for detecting local text reuses at the semantic level	Qi Zhang, Jihua Kang, Jin Qian, Xuanjing Huang	In this paper, we introduce a novel method to efficiently detect local reuses at the semantic level for large scale problems.
84	CiteSight: supporting contextual citation recommendation using differential search	Avishay Livne, Vivek Gokuladas, Jaime Teevan, Susan T. Dumais, Eytan Adar	In this paper we explore how a range of search needs and expectations can be supported within a single search system using differential search.
85	Cross-language context-aware citation recommendation in scientific articles	Xuewei Tang, Xiaojun Wan, Xun Zhang	In this paper, we define a novel task of cross-language context-aware citation recommendation, which aims at recommending English citations for a given context of the place where a citation is made in a Chinese paper.
86	Search result diversification via data fusion	Shengli Wu, Chunlan Huang	In this short paper, we propose a few data fusion methods to try to improve performance when both relevance and diversity are concerned.
87	Hashtag recommendation for hyperlinked tweets	Surendra Sedhai, Aixin Sun	In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages).
88	Personalized document re-ranking based on Bayesian probabilistic matrix factorization	Fei Cai, Shangsong Liang, Maarten de Rijke	Using search logs from a commercial search engine, we (i) investigate the impact of features derived from user behavior on reranking a generic ranked list; (ii) optimally integrate the contributions of user behavior and candidate documents by learning their relative importance per query based on similar users.
89	Using the cross-entropy method to re-rank search results	Haggai Roitman, Shay Hummel, Oren Kurland	We present a novel unsupervised approach to re-ranking an initially retrieved list.
90	Computing and applying topic-level user interactions in microblog recommendation	Xiao Lu, Peng Li, Hongyuan Ma, Shuxin Wang, Anying Xu, Bin Wang	To explore the effects of behavior based relationship on recommendation, we propose an Interaction Based Collaborative Filtering (IBCF) approach.
91	Towards context-aware search with right click	Aixin Sun, Chii-Hian Lou	To this end, we evaluate 7 text component extraction schemes, and 5 feature extraction schemes.
92	Rendering expressions to improve accuracy of relevance assessment for math search	Matthias S. Reichenbach, Anurag Agarwal, Richard Zanibbi	We designed a study where participants completed search tasks involving mathematical expressions using two different summary styles, and measured response time and relevance assessment accuracy.
93	Exploring recommendations in internet of things	Lina Yao, Quan Z. Sheng, Anne H.H. Ngu, Helen Ashman, Xue Li	In this paper, we focus on the things recommendation problem in Internet of Things (IoT).
94	Sig-SR: SimRank search over singular graphs	Weiren Yu, Julie A. McCann	In this paper, we provide a treatment of [1], by supporting similarity assessment on non-invertible adjacency matrices.
95	Old dogs are great at new tricks: column stores for ir prototyping	Hannes Mühleisen, Thaer Samar, Jimmy Lin, Arjen de Vries	We make the suggestion that instead of implementing custom index structures and query evaluation algorithms, IR researchers should simply store document representations in a column-oriented relational database and implement ranking models using SQL.
96	The role of network distance in linkedin people search	Shih-Wen Huang, Daniel Tunkelang, Karrie Karahalios	This paper presents insights about people search behavior on LinkedIn, based on a log analysis and a user study.
97	Latent community discovery through enterprise user search query modeling	Kevin M. Carter, Rajmonda S. Caceres, Ben Priest	In this work, we present a two-step framework for classifying user behavior within an enterprise in a data-driven way.
98	Examining collaborative query reformulation: a case of travel information searching	Abu Shamim Mohammad Arif, Jia Tina Du, Ivan Lee	This paper investigates query reformulation behavior in collaborative tourism information searching on the Web.
99	Influential nodes selection: a data reconstruction perspective	Zhefeng Wang, Hao Wang, Qi Liu, Enhong Chen	In this paper, we view the influence maximization problem from the perspective of data reconstruction and propose a novel framework named \textsl{Data Reconstruction for Influence Maximization}(DRIM).
100	A fusion approach to cluster labeling	Haggai Roitman, Shay Hummel, Michal Shmueli-Scheuer	We present a novel approach to the cluster labeling task using fusion methods.
101	Evaluating the effort involved in relevance assessments for images	Martin Halvey, Robert Villa	In this paper, we focus on the process by which relevance is judged for images, and in particular, the degree of effort a user must expend to judge relevance for different topics.
102	Diversifying query suggestions based on query documents	Youngho Kim, W. Bruce Croft	To help users in this situation, we propose a method to suggest diverse queries that can cover multiple aspects of the query document.
103	Comparing client and server dwell time estimates for click-level satisfaction prediction	Youngho Kim, Ahmed Hassan, Ryen W. White, Imed Zitouni	In this paper, we define three different dwell times, i.e., server-side, client-side, and trail dwell time, and examine their effectiveness for predicting click satisfaction.
104	Score-safe term-dependency processing with hybrid indexes	Matthias Petri, Alistair Moffat, J. Shane Culpepper	We present a hybrid approach which leverages score-safe processing and suffix-based self-indexing structures in order to provide efficient and effective top-k document retrieval.
105	Co-training on authorship attribution with very fewlabeled examples: methods vs. views	Tieyun Qian, Bing Liu, Ming Zhong, Guoliang He	In this paper, we present a novel two-view co-training framework to iteratively identify the authors of a few unlabeled data to augment the training set.
106	Probabilistic text modeling with orthogonalized topics	Enpeng Yao, Guoqing Zheng, Ou Jin, Shenghua Bao, Kailong Chen, Zhong Su, Yong Yu	In this paper, we propose the Orthogonalized Topic Model(OTM) which imposes an orthogonality constraint on the topic-term distributions.
107	Evaluating non-deterministic retrieval systems	Gaya K. Jayasinghe, William Webber, Mark Sanderson, Lasitha S. Dharmasena, J. Shane Culpepper	Using the context of distributed information retrieval as a case study for our investigation, we propose a solution based on multivariate linear modeling.
108	Extending test collection pools without manual runs	Gaya K. Jayasinghe, William Webber, Mark Sanderson, J. Shane Culpepper	In this work, we explore fully automated approaches to generating a pool.
109	The search duel: a response to a strong ranker	Peter Izsak, Fiana Raiber, Oren Kurland, Moshe Tennenholtz	We present a per-query algorithmic approach that leverages fundamental retrieval principles such as pseudo-feedback-based relevance modeling.
110	Modeling the evolution of product entities	Priya Radhakrishnan, Manish Gupta, Vasudeva Varma	In this paper, we tackle the problem of finding the previous version (predecessor) of a product entity.
111	Predicting bursts and popularity of hashtags in real-time	Shoubin Kong, Qiaozhu Mei, Ling Feng, Fei Ye, Zhe Zhao	In this paper, we study the problems of real-time prediction of bursting hashtags.
112	Probabilistic ensemble learning for vietnamese word segmentation	Wuying Liu, Li Lin	This paper addresses the problem of Vietnamese word segmentation, proposes a probabilistic ensemble learning (PEL) framework, and designs a novel PEL-based word segmentation (PELWS) algorithm.
113	Improving unsupervised query segmentation using parts-of-speech sequence information	Rishiraj Saha Roy, Yogarshi Vyas, Niloy Ganguly, Monojit Choudhury	We present a generic method for augmenting unsupervised query segmentation by incorporating Parts-of-Speech (POS) sequence information to detect meaningful but rare n-grams.
114	Building a query log via crowdsourcing	Omar Alonso, Maria Stone	We explore a different alternative based on human computation to gather a bit more information from users and show the type of query log that would be possible to construct.
115	Web search without ‘stupid’ results	Aleksandra Lomakina, Nikita Povarov, Pavel Serdyukov	So, we attempted to find a method to determine such documents and reduce their negative impact upon users and, as a consequence, on search engines in general.
116	Discovering real-world use cases for a multimodal math search interface	Keita Del Valle Wangari, Richard Zanibbi, Anurag Agarwal	We present a user study examining whether min changes search behavior for mathematical non-experts, and to identify real-world usage scenarios for multimodal math search interfaces.
117	Improving search personalisation with dynamic group formation	Thanh Tien Vu, Dawei Song, Alistair Willis, Son Ngoc Tran, Jingfei Li	In this paper, we argue that common interest groups should be dynamically constructed in response to the user’s input query.
118	Detection of abnormal profiles on group attacks in recommender systems	Wei Zhou, Yun Sing Koh, Junhao Wen, Shafiq Alam, Gillian Dobbie	We propose a novel technique for identifying group attack profiles which uses an improved metric based on Degree of Similarity with Top Neighbors (DegSim) and Rating Deviation from Mean Agreement (RDMA).
119	On run diversity in Evaluation as a Service	Ellen M. Voorhees, Jimmy Lin, Miles Efron	This paper shows that the distinctiveness of the retrieval runs used to construct the first test collection built using EaaS, the TREC 2013 Microblog collection, is not substantially different from that of the TREC-8 ad hoc collection, a high-quality collection built using traditional pooling.
120	Evaluating answer passages using summarization measures	Mostafa Keikha, Jae Hyun Park, W. Bruce Croft	In this paper, we describe the advantages of document summarization measures for evaluating answer passage retrieval and show that these measures have high correlation with existing measures and human judgments.
121	Analyzing bias in CQA-based expert finding test sets	Reyyan Yeniterzi, Jamie Callan	A more bias free test set construction approach, which has correlated results with the manual assessments, is also proposed in this paper.
122	Understanding negation and family history to improve clinical information retrieval	Bevan Koopman, Guido Zuccon	We present a study to understand the effect that negated terms (e.g., "no fever") and family history (e.g., "family his- tory of diabetes") have on searching clinical records.
123	Modeling dual role preferences for trust-aware recommendation	Weilong Yao, Jing He, Guangyan Huang, Yanchun Zhang	In this paper, we propose to learn dual role preferences (truster/trustee-specific preferences) for trust-aware recommendation by modeling explicit interactions (e.g., rating and trust) and implicit interactions.
124	Mouse movement during relevance judging: implications for determining user attention	Mark D. Smucker, Xiaoyu Sunny Guo, Andrew Toulis	We found that in a large number of cases, the users did nothing more with their mouse than move it to the buttons used for recording the relevance decision.
125	PAAP: prefetch-aware admission policies for query results cache in web search engines	Hongyuan Ma, Wei Liu, Bingjie Wei, Liang Shi, Xiuguo Bao, Lihong Wang, Bin Wang	In this paper we present two novel admission policies tailored for query results cache.
126	User geospatial context for music recommendation in microblogs	Markus Schedl, Andreu Vall, Katayoun Farrahi	In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recommendation.
127	Compositional data analysis (CoDA) approaches to distance in information retrieval	Paul Thomas, David Lovell	In this work we explore compositional data in IR through the lens of distance measures, and demonstrate that common measures, naive to compositions, have some undesirable properties which can be avoided with composition-aware measures.
128	Group latent factor model for recommendation with multiple user behaviors	Jian Cheng, Ting Yuan, Jinqiao Wang, Hanqing Lu	To address this problem, we propose a novel recommendation model, named Group Latent Factor Model (GLFM), which attempts to learn a factorization of latent factor space into subspaces that are shared across multiple behaviors and subspaces that are specific to each type of behaviors.
129	Hashing with List-Wise learning to rank	Zhou Yu, Fei Wu, Yin Zhang, Siliang Tang, Jian Shao, Yueting Zhuang	In this paper, we consider the hashing problem from the perspective of optimizing a list-wise learning to rank problem and propose an approach called List-Wise supervised Hashing (LWH).
130	A burstiness-aware approach for document dating	Dimitrios Kotsakos, Theodoros Lappas, Dimitrios Kotzias, Dimitrios Gunopulos, Nattiya Kanhabua, Kjetil Nørvåg	In this paper, we study the task of approximating the timestamp of a doc- ument, so-called document dating.
131	An analysis of query difficulty for information retrieval in the medical domain	Lorraine Goeuriot, Liadh Kelly, Johannes Leveling	We present a post-hoc analysis of a benchmarking activity for information retrieval (IR) in the medical domain to determine if performance for queries with different levels of complexity can be associated with different IR methods or techniques.
132	Mobile query reformulations	Milad Shokouhi, Rosie Jones, Umut Ozertem, Karthik Raghunathan, Fernando Diaz	In this paper, we study the query reformulation patterns in mobile logs.
133	On peculiarities of positional effects in sponsored search	Vyacheslav Alipov, Valery Topinsky, Ilya Trofimov	In this paper we show the strong evidence that this practice is far from perfection when considering the top ads block on a search engine result page (SERP).
134	A collective topic model for milestone paper discovery	Ziyu Lu, Nikos Mamoulis, David W. Cheung	In this paper, we study the automatic discovery of the core papers for a research area.
135	Document summarization based on word associations	Oskar Gross, Antoine Doucet, Hannu Toivonen	In this paper we propose a novel, unsupervised method for (multi-)document summarization.
136	Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification	Yongfeng Zhang, Haochen Zhang, Min Zhang, Yiqun Liu, Shaoping Ma	In this paper, we attempt to bridge the gap between phrase-level and review/document-level sentiment analysis by leveraging the results given by review-level sentiment classification to boost phrase-level sentiment polarity labeling in contextual sentiment lexicon construction tasks, using a novel constrained convex optimization framework.
137	Random subspace for binary codes learning in large scale image retrieval	Cong Leng, Jian Cheng, Hanqing Lu	In this work, we introduce a random subspace strategy to address this limitation.
138	Incorporating query-specific feedback into learning-to-rank models	Ethem F. Can, W. Bruce Croft, R. Manmatha	In this work, we expand this common way by focusing on an approach that enables us to do query-specific modification of a retrieval model for learning-to-rank problems.
139	Large-scale author verification: temporal and topical influences	Michiel van Dam, Claudia Hauff	In this work, we present a methodology to derive a large-scale author verification corpus based on Wikipedia Talkpages. We create a corpus based on English Wikipedia which is significantly larger than existing corpora.
140	Evaluating mobile web search performance by taking good abandonment into account	Olga Arkhipova, Lidia Grauer	This article provides an offline metric for quality evaluation of mobile Web search, which takes good abandonment rate into consideration.
141	Assessing the reliability and reusability of an E-discovery privilege test collection	Jyothi K. Vinjumur, Douglas W. Oard, Jiaul H. Paik	This paper examines the reliability and reusability of that collection.
142	Modeling evolution of a social network using temporalgraph kernels	Akash Anil, Niladri Sett, Sanasam Ranbir Singh	In this paper, we propose temporal spectral graph kernels of four popular graph kernels namely path counting, triangle closing, exponential and neumann.
143	On user interactions with query auto-completion	Bhaskar Mitra, Milad Shokouhi, Filip Radlinski, Katja Hofmann	In this paper, we present the first large-scale study of user interactions with auto-completion based on query logs of Bing, a commercial search engine.
144	Re-ranking approach to classification in large-scale power-law distributed category systems	Rohit Babbar, Ioannis Partalas, Eric Gaussier, Massih-reza Amini	In this work, we exploit the distribution of documents among categories to (i) derive an upper bound on the accuracy of any classifier, and (ii) propose a ranking-based algorithm which aims to maximize this upper bound.
145	Enhancing personalization via search activity attribution	Adish Singla, Ryen W. White, Ahmed Hassan, Eric Horvitz	We propose enhancing Web search personalization with methods that can disambiguate among different users of a machine, thus connecting the current query with the appropriate search history.
146	A syntax-aware re-ranker for microblog retrieval	Aliaksei Severyn, Alessandro Moschitti, Manos Tsagkias, Richard Berendsen, Maarten de Rijke	We tackle the problem of improving microblog retrieval algorithms by proposing a robust structural representation of (query, tweet) pairs.
147	Weighted aspect-based collaborative filtering	YanPing Nie, Yang Liu, Xiaohui Yu	In this paper, we propose a method that uses tensor factorization to automatically infer the weights of different aspects in forming the overall rating.
148	Evaluating intuitiveness of vertical-aware click models	Aleksandr Chuklin, Ke Zhou, Anne Schuth, Floor Sietsma, Maarten de Rijke	We propose a method for evaluating the intuitiveness of vertical-aware click models, namely the ability of a click model to capture key aspects of aggregated result pages, such as vertical selection, item selection, result presentation and vertical diversity.
149	Recipient recommendation in enterprises using communication graphs and email content	David Graus, David van Dijk, Manos Tsagkias, Wouter Weerkamp, Maarten de Rijke	We propose an intuitive and elegant way of modeling the task of recipient recommendation, which uses both the communication graph (i.e., who are most closely connected to the sender) and the content of the email.
150	Analyzing the content emphasis of web search engines	Mohammed A. Alam, Doug Downey	We present PAWS, a platform for analyzing differences among Web search engines.
151	Effects of task and domain on searcher attention	Dmitry Lagun, Eugene Agichtein	In this paper we present, to best of our knowledge, the first cross-domain comparison of search examination behavior and patterns of aggregated attention across Web Search, News, Shopping and Social Network domains.
152	Learning sufficient queries for entity filtering	Miles Efron, Craig Willis, Garrick Sherman	In this paper, we present a simple yet effective approach based on learning high-quality Boolean queries that can be applied deterministically during filtering.
153	PatentLine: analyzing technology evolution on multi-view patent graphs	Longhui Zhang, Lei Li, Tao Li, Qi Zhang	In this paper, we propose a unified framework, named PatentLine, to generate a technology evolution tree for a given topic or a classification code related to granted patents.
154	Query performance prediction for entity retrieval	Hadas Raviv, Oren Kurland, David Carmel	We address the query-performance-prediction task for entity retrieval; that is, retrieval effectiveness is estimated with no relevance judgements.
155	Second order probabilistic models for within-document novelty detection in academic articles	Laurence A.F. Park, Simeon Simoff	In this article, we propose the concept of Within Document Novelty Location, a method of identifying locations of novelty and non-novelty within a given document.
156	Modeling the dynamics of personal expertise	Yi Fang, Archana Godavarthy	In this paper, we propose a probabilistic model to characterize how people change or stick with their expertise.
157	An annotation similarity model in passage ranking for historical fact validation	Jun Araki, Jamie Callan	We propose a combination of a traditional bag-of-words similarity model and an annotation similarity model to improve passage ranking.
158	To hint or not: exploring the effectiveness of search hints for complex informational tasks	Denis Savenkov, Eugene Agichtein	This work describes the results of a controlled user study comparing the effects of provid- ing specific vs. generic search hints on search success and satisfaction.
159	The effect of sampling strategy on inferred measures	Ellen M. Voorhees	This paper addresses this gap by examining the effect on collection quality of different sampling strategies within the inferred measures framework.
160	Cache-conscious runtime optimization for ranking ensembles	Xun Tang, Xin Jin, Tao Yang	We propose a 2D blocking scheme for better cache utilization with simpler code structure compared to previous work.
161	Bridging temporal context gaps using time-aware re-contextualization	Andrea Ceroni, Nam Khanh Tran, Nattiya Kanhabua, Claudia Niederée	In this paper, we study time-aware re-contextualization, the challenging problem of retrieving concise and complementing information in order to bridge this temporal context gap.
162	An enhanced context-sensitive proximity model for probabilistic information retrieval	Jiashu Zhao, Jimmy Xiangji Huang	We propose to enhance proximity-based probabilistic retrieval models with more contextual information.
163	On the information difference between standard retrieval models	Peter B. Golbus, Javed A. Aslam	Recent work introduced a probabilistic framework that measures search engine performance information-theoretically.
164	A POMDP model for content-free document re-ranking	Sicong Zhang, Jiyun Luo, Hui Yang	In this paper, we propose to model log-based document re-ranking as a Partially Observable Markov Decision Process (POMDP).
165	Using score differences for search result diversification	Sadegh Kharazmi, Mark Sanderson, Falk Scholer, David Vallet	We introduce a novel post-retrieval approach, which is independent of external information or even the full-text content of retrieved documents; only the retrieval score of a document is used.
166	TREC: topic engineering exercise	J Shane Culpepper, Stefano Mizzaro, Mark Sanderson, Falk Scholer	In this work, we investigate approaches to engineer better topic sets in information retrieval test collections.
167	How k-12 students search for learning?: analysis of an educational search engine log	Arif Usta, Ismail Sengor Altingovde, İbrahim Bahattin Vidinli, Rifat Ozcan, Özgür Ulusoy	In this study, we analyze an educational search engine log for shedding light on K-12 students’ search behavior in a learning environment.
168	The correlation between cluster hypothesis tests and the effectiveness of cluster-based retrieval	Fiana Raiber, Oren Kurland	We present a study of the correlation between the extent to which the cluster hypothesis holds, as measured by various tests, and the relative effectiveness of cluster-based retrieval with respect to document-based retrieval.
169	The effect of expanding relevance judgements with duplicates	Gaurav Baruah, Adam Roegiest, Mark D. Smucker	We examine the effects of expanding a judged set of sentences with their duplicates from a corpus.
170	On correlation of absence time and search effectiveness	Sunandan Chakraborty, Filip Radlinski, Milad Shokouhi, Paul Baecke	In this paper, we investigate the effectiveness of absence time in evaluating new features in a web search engine, such as new ranking algorithm or a new user interface.
171	Necessary and frequent terms in queries	Jiepu Jiang, James Allan	Specifically, we study two distinct types of terms that exist in all search queries: (1) necessary terms, for which term occurrence alone is indicative of document relevance; and (2) frequent terms, for which the relative term frequency is indicative of document relevance within the set of documents where the term appears.
172	Extracting topics based on authors, recipients and content in microblogs	Nazneen Fatema N. Rajani, Kate McArdle, Jason Baldridge	In this paper, we demonstrate the application of the Author-Topic and the Author-Recipient-Topic model to microblogs.
173	Exploiting Twitter and Wikipedia for the annotation of event images	Philip James McParlane, Joemon Jose	In this paper, we develop an image annotation model which exploits textual content from related Twitter and Wikipedia data which aims to overcome the discussed problems.
174	Learning to translate queries for CLIR	Artem Sokolov, Felix Hieber, Stefan Riezler	In this paper we propose a decomposable proxy for retrieval quality that obviates the need for costly intermediate retrieval.
175	Predicting query performance in microblog retrieval	Jesus A. Rodriguez Perez, Joemon M. Jose	In this work we study the performance of the state of the art predictors under microblog retrieval conditions as well as introducing our own predictors.
176	An event extraction model based on timeline and user analysis in Latent Dirichlet allocation	Bayar Tsolmon, Kyung-Soon Lee	This paper proposes an event extraction method which combines user reliability and timeline analysis.
177	What makes data robust: a data analysis in learning to rank	Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Xiubo Geng	In our work, we investigate what inherent characteristics make training data robust to label noise.
178	Learning to bridge colloquial and formal language applied to linking and search of E-Commerce data	Ivan Vulić, Susana Zoghbi, Marie-Francine Moens	We propose a novel probabilistic topic model called multi-idiomatic LDA (MiLDA).
179	Uncovering the unarchived web	Thaer Samar, Hugo C. Huurdeman, Anat Ben-David, Jaap Kamps, Arjen de Vries	We present a method to create representations of what we will refer to as a web collection’s (aura): the web documents that were not included in the archived collection, but are known to have existed — due to their mentions on pages that were included in the archived web collection.
180	Inferring topic-dependent influence roles of Twitter users	Chengyao Chen, Dehong Gao, Wenjie Li, Yuexian Hou	In this paper, we move a step forward trying to further distinguish influence roles of Twitter users in a certain topic.
181	Reputation analysis with a ranked sentiment-lexicon	Filipa Peleja, João Santos, João Magalhães	In this paper we describe an unsupervised method for performing a simultaneous-analysis of the reputation of multiple named-entities.
182	On predicting religion labels in microblogging networks	Minh-Thap Nguyen, Ee-Peng Lim	In this paper, we study the problem of predicting users’ religion labels using their microblogging data.
183	Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream	Xiaoyang Wang, Ying Zhang, Wenjie Zhang, Xuemin Lin	Particularly, we formally introduce the problem of identifying local frequent keyword co-occurrence patterns over the geo-tagged Twitter streams, namely LFP\xspace query.
184	Item group based pairwise preference learning for personalized ranking	Shuang Qiu, Jian Cheng, Ting Yuan, Cong Leng, Hanqing Lu	In this paper, we exploit this prior information of a user’s preference from the nearest neighbor set by the neighbors’ implicit feedbacks, which can split items into different item groups with specific ranking relations.
185	Where not to go?: detecting road hazards using twitter	Avinash Kumar, Miao Jiang, Yi Fang	In this paper, we demonstrate an application of Twitter to atomically determining road hazards.
186	Enhancing sketch-based sport video retrieval by suggesting relevant motion paths	Ihab Al Kabary, Heiko Schuldt	In this paper, we present an auto-suggest search feature that harnesses spatiotemporal data of team sport videos to suggest potential directions containing relevant data during the formulation of a sketch-based motion query.
187	Dynamic location models	Vanessa Murdock	In this work we propose an algorithm for constructing hyperlocal models of places that are as small as half a city block.
188	Wikipedia-based query performance prediction	Gilad Katz, Anna Shtock, Oren Kurland, Bracha Shapira, Lior Rokach	We propose a {\em corpus-independent} approach to pre-retrieval prediction which relies on information extracted from Wikipedia.
189	A revisit to social network-based recommender systems	Hui Li, Dingming Wu, Nikos Mamoulis	In this paper, we propose two methods to improve the performance of the state-of-art social network-based recommender system (SNRS), which is based on a probabilistic model.
190	Relevation!: An open source system for information retrieval relevance assessment	Bevan Koopman, Guido Zuccon	Relevation!: An open source system for information retrieval relevance assessment
191	WenZher: comprehensive vertical search for healthcare domain	Liqiang Nie, Tao Li, Mohammad Akbari, Jialie Shen, Tat-Seng Chua	WenZher: comprehensive vertical search for healthcare domain
192	STICS: searching with strings, things, and cats	Johannes Hoffart, Dragan Milchevski, Gerhard Weikum	This paper describes an advanced search engine that supports users in querying documents by means of keywords, entities, and categories.
193	VIRLab: a web-based virtual lab for learning and studying information retrieval models	Hui Fang, Hao Wu, Peilin Yang, ChengXiang Zhai	In this paper, we describe VIRLab, a novel web-based virtual laboratory for Information Retrieval (IR).
194	ServiceXplorer: a similarity-based web service search engine	Anne H.H. Ngu, Jiangang Ma, Quan Z. Sheng, Lina Yao, Scott Julian	We demonstrate in this paper that by utilizing well known indexing scheme such as inverted file and R-tree indexes over Web services attributes, the Earth Mover’s Distance (EMD) algorithm can be used efficiently to find partial matches between a query and a database of Web services.
195	Real-time visualization and targeting of online visitors	Deepak Pai, Sandeep Zechariah George	We show that dynamic visitor attributes extracted from their click-stream provide much better predictive capabilities of visitor intent.
196	CharBoxes: a system for automatic discovery of character infoboxes from books	Manish Gupta, Piyush Bansal, Vasudeva Varma	As part of this demo, we design mechanisms to address these challenges and experiment with publicly available books.
197	ADAM: a system for jointly providing ir and database queries in large-scale multimedia retrieval	Ivan Giangreco, Ihab Al Kabary, Heiko Schuldt	In this paper, we introduce ADAM, a system that is able to store and retrieve multimedia objects by seamlessly combining aspects from databases and information retrieval.
198	NicePic!: a system for extracting attractive photos from flickr streams	Sergej Zerr, Stefan Siersdorfer, Jose San Pedro, Jonathon Hare, Xiaofei Zhu	In this demonstration we show a novel application which automatically classifies images in a live photo stream according to their attractiveness for the community, based on a number of visual and textual features.
199	A perspective-aware approach to search: visualizing perspectives in news search results	Muhammad Atif Qureshi, Colm O’Riordan, Gabriella Pasi	This demonstration paper presents a system that allows users to specify at query time a perspective together with their query.
200	FitYou: integrating health profiles to real-time contextual suggestion	Christopher Wing, Hui Yang	We introduce the mobile application FitYou, which dynamically generates recommendations according to the user’s current location and health condition as a real-time LBS.
201	Semantic full-text search with broccoli	Hannah Bast, Florian Bäurle, Björn Buchhold, Elmar Haußmann	We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}.
202	Just-for-me: an adaptive personalization system for location-aware social music recommendation	Zhiyong Cheng, Jialie Shen, Tao Mei	In this demonstration, we present an intelligent system, called Just-for-Me, to facilitate accurate music recommendation based on where user presents.
203	A novel system for the semi automatic annotation of event images	Philip James McParlane, Joemon Jose	Specifically, we present a novel tag recommendation system for images taken at a popular music festival which allows the user to select relevant tags from related Tweets and Wikipedia content, thus reducing the workload involved in the annotation process.
204	An interactive interface for visualizing events on Twitter	Andrew J. McMinn, Daniel Tsvetkov, Tsvetan Yordanov, Andrew Patterson, Rrobi Szk, Jesus A. Rodriguez Perez, Joemon M. Jose	We have developed an interactive interface for visualizing events, backed by a state-of-the-art event detection approach, which is able to detect, track and summarize events in real-time.
205	ExperTime: tracking expertise over time	Jan Rybak, Krisztian Balog, Kjetil Nørvåg	This paper presents ExperTime, a web-based system for tracking expertise over time.
206	Cluster links prediction for literature based discovery using latent structure and semantic features	Yakub Sebastian	We introduce a novel algorithm, Latent Domain Similarity (LDS), which uses combinations of semantic features (e.g. distribution of technical terms in titles and abstracts) and structural features (e.g. cited references, citing articles) of two or more articles in order to infer shared latent domains between them.
207	Graph-based large scale RDF data compression	Wei Emma Zhang	We propose a two-stage lossless compression approach on large scale RDF data.
208	Entity-based retrieval	Hadas Raviv	We address the core challenge of the entity retrieval task: ranking entities in response to a query by their presumed relevance to the information need that the query represents.
209	Improving offline and online web search evaluation by modelling the user behaviour	Eugene Kharitonov	Improving offline and online web search evaluation by modelling the user behaviour
210	Modelling of terms across scripts through autoencoders	Parth Gupta	Very recently we have formally defined the problem of MSIR and presented the quantitative study on it through Bing query log analysis.
211	A tag-based personalized item recommendation system using tensor modeling and topic model approaches	Noor Ifada	This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach.
212	Novelty and diversity enhancement and evaluation in recommender systems and information retrieval	Saúl Vargas	In this doctoral research we study the assessment and enhancement of both properties in the confluence of Information Retrieval and Recommender Systems.
213	Enrichment of user profiles across multiple online social networks for volunteerism matching for social enterprise	Xuemeng Song	In this work, we aim to bridge the gap between the supply of talents with volunteering tendency and the demands of social enterprise and enhance the social welfare.
214	Choices and constraints: research goals and approaches in information retrieval (part 1)	Diane Kelly, Filip Radlinski, Jaime Teevan	Participants will come away with a broad perspective of research goals and approaches in IR, and an understanding of the benefits and limitations of these research approaches.
215	Choices and constraints: research goals and approaches in information retrieval (part 2)	Diane Kelly, Filip Radlinski, Jaime Teevan	Participants will come away with a broad perspective of research goals and approaches in IR, and an understanding of the benefits and limitations of these research approaches.
216	Scalability and efficiency challenges in large-scale web search engines	B. Barla Cambazoglu, Ricardo Baeza-Yates	The main objective of this tutorial is to provide an overview of the fundamental scalability and efficiency challenges in commercial web search engines, bridging the existing gap between the industry and academia.
217	Statistical significance testing in information retrieval: theory and practice	Ben Carterette	Statistical significance testing in information retrieval: theory and practice
218	Speech search: techniques and tools for spoken content retrieval	Gareth J.F. Jones	Speech search: techniques and tools for spoken content retrieval
219	Axiomatic analysis and optimization of information retrieval models	Hui Fang, ChengXiang Zhai	Axiomatic approach provides a systematic way to think about heuristics, identify the weakness of existing methods, and optimize the existing methods accordingly.
220	A general account of effectiveness metrics for information tasks: retrieval, filtering, and clustering	Enrique Amigó, Julio Gonzalo, Stefano Mizzaro	In this tutorial we will present, review, and compare the most popular evaluation metrics for some of the most salient information related tasks, covering: (i) Information Retrieval, (ii) Clustering, and (iii) Filtering.
221	Dynamic information retrieval modeling	Hui Yang, Marc Sloan, Jun Wang	The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change.
222	The retrievability of documents	Leif Azzopardi	In this tutorial, we shall explain the concept of retrievability along with a number of retrievability measures, how it can be estimated and how it can be used for analysis.
223	ERD’14: entity recognition and disambiguation challenge	David Carmel, Ming-Wei Chang, Evgeniy Gabrilovich, Bo-June (Paul) Hsu, Kuansan Wang	ERD’14: entity recognition and disambiguation challenge
224	SIGIR 2014 workshop on gathering efficient assessments of relevance (GEAR)	Martin Halvey, Robert Villa, Paul Clough	This workshop revisits how relevance assessments can be efficiently created, seeking to provide a forum for discussion and exploration of the topic.
225	MedIR14: medical information retrieval workshop	Lorraine Goeuriot, Gareth J.F. Jones, Liadh Kelly, Henning Müller, Justin Zobel	The aim of the workshop is to bring together researchers interested in medical information search with the goal of identifying specific challenges that need to be addressed to advance the state-of-the-art.
226	Privacy-preserving IR: when information retrieval meets privacy and security	Luo Si, Hui Yang	This privacy-preserving IR workshop aims to spur research that brings together the research fields of IR and privacy/security, and research that mitigates privacy threats in information retrieval by constructing novel algorithms and tools that enable web users to better understand associated privacy risks.
227	SIGIR 2014 workshop on semantic matching in information retrieval	Julio Gonzalo, Hang Li, Alessandro Moschitti, Jun Xu	This simple approach works rather well in practice, partly because there are many other signals in web search (hypertext, user logs, etc.) that complement it.
228	SoMeRA 2014: social media retrieval and analysis workshop	Markus Schedl, Peter Knees, Jialie Shen	SoMeRA 2014: social media retrieval and analysis workshop
229	SIGIR 2014 workshop on temporal, social and spatially-aware information access (#TAIA2014)	Fernando Diaz, Claudia Hauff, Vanessa Murdock, Maarten de Rijke, Milad Shokouhi	SIGIR 2014 workshop on temporal, social and spatially-aware information access (#TAIA2014)