Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Clustering versus faceted categories for information exploration
Communications of the ACM - Supporting exploratory search
Ranking objects based on relationships
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Measure-driven keyword-query expansion
Proceedings of the VLDB Endowment
Context-aware citation recommendation
Proceedings of the 19th international conference on World wide web
Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia
Proceedings of the 19th international conference on World wide web
Faceted exploration of image search results
Proceedings of the 19th international conference on World wide web
Automatically building research reading lists
Proceedings of the fourth ACM conference on Recommender systems
Who should I cite: learning literature search models from citation behavior
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
I am complex: cluster me, don't just rank me
Proceedings of the 2nd International Workshop on Business intelligencE and the WEB
Approximation algorithms for the weighted independent set problem
WG'05 Proceedings of the 31st international conference on Graph-Theoretic Concepts in Computer Science
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Keyword based search interfaces are extremely popular as a means for efficiently discovering items of interest from a huge collection, as evidenced by the success of search engines like Google and Bing. However, most of the current search services still return results as a flat ranked list of items. Considering the huge number of items which can match a query, this list based interface can be very difficult for the user to explore and find important items relevant to their search needs. In this work, we consider a search scenario in which each item is annotated with a set of keywords. E.g., in Web 2.0 enabled systems such as flickr and del.icio.us, it is common for users to tag items with keywords. Based on this annotation information, we can automatically group query result items into different expansions of the query corresponding to subsets of keywords. We formulate and motivate this problem within a top-k query processing framework, but as that of finding the top-k most important expansions. Then we study additional desirable properties for the set of expansions returned, and formulate the problem as an optimization problem of finding the best k expansions satisfying all the desirable properties. We propose several efficient algorithms for this problem. Our problem is similar in spirit to recent works on automatic facets generation, but has the important difference and advantage that we don't need to assume the existence of pre-defined categorical hierarchy which is critical for these works. Through extensive experiments on both real and synthetic datasets, we show our proposed algorithms are both effective and efficient.