Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Post-rank reordering: resolving preference misalignments between search engines and end users
Proceedings of the 18th ACM conference on Information and knowledge management
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Learning social tag relevance by neighbor voting
IEEE Transactions on Multimedia
How to ConQueR why-not questions
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Image tag refinement towards low-rank, content-tag prior and error sparsity
Proceedings of the international conference on Multimedia
Result diversification based on query-specific cluster ranking
Journal of the American Society for Information Science and Technology
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
Encyclopedia of Parallel Computing
Encyclopedia of Parallel Computing
Towards a Relevant and Diverse Search of Social Images
IEEE Transactions on Multimedia
Tag-based social image retrieval: An empirical evaluation
Journal of the American Society for Information Science and Technology
Answering Why-not Questions on Top-k Queries
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Evaluating implicit judgments from image search clickthrough data
Journal of the American Society for Information Science and Technology
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Despite considerable progress in recent years on Tag-based Social Image Retrieval (TagIR), state-of-the-art TagIR systems fail to provide a systematic framework for end users to ask why certain images are not in the result set of a given query and provide an explanation for such missing results. However, as humans, such why-not questions are natural when expected images are missing in the query results returned by a TagIR system. Clearly, it would be very helpful to users if they could pose follow-up why-not questions to seek clarifications on missing images in query results. In this work, we take the first step to systematically answer the why-not questions posed by end-users on TagIR systems. Our answer not only involves the reason why desired images are missing in the results but also suggestion on how the query can be altered so that the user can view these missing images in sufficient number. We present three explanation models, namely result reordering, query relaxation, and query substitution, that enable us to explain a variety of why-not questions. We present an algorithm called WINE (Why-not questIon aNswering Engine) that exploits these models to answer why-not questions efficiently. Experiments on NUS-WIDE dataset demonstrate effectiveness as well as benefits of WINE.