Visualizing digital library search results with categorical and hierarchical axes
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
User Modeling and User-Adapted Interaction
Evaluation and evolution of a browse and search interface: Relation Browser++
dg.o '05 Proceedings of the 2005 national conference on Digital government research
Automatic construction of multifaceted browsing interfaces
Proceedings of the 14th ACM international conference on Information and knowledge management
Exploring digital libraries: integrating browsing, searching, and visualization
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Supporting exploratory web search with meaningful and stable categorized overviews
Supporting exploratory web search with meaningful and stable categorized overviews
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A probability ranking principle for interactive information retrieval
Information Retrieval
Personalized interactive faceted search
Proceedings of the 17th international conference on World Wide Web
Dynamic faceted search for discovery-driven analysis
Proceedings of the 17th ACM conference on Information and knowledge management
Facet selection algorithms for web product search
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Multifaceted search is a popular interaction paradigm that allows users to analyze and navigate through multidimensional data. A crucial aspect of faceted search applications is selecting the list of facets to display to the user following each query. We call this the facet selection problem. When refining a query by drilling down into a facet, documents that are associated with that facet are promoted in the rankings, as better-ranking documents not associated with the facet are filtered out. We formulate facet selection as an optimization problem aiming to maximize the rank promotion of certain documents. As the optimization problem is NP-Hard, we propose an approximation algorithm for selecting an approximately optimal set of facets per query. We conducted experiments over hundreds of queries and search results of a large commercial search engine, comparing two flavors of our algorithm to facet selection algorithms appearing in the literature. The results show that our algorithm significantly outperforms those baseline schemes.