Multi-select faceted navigation based on minimum description length principle

  • Authors:

  • Chao He;Xueqi Cheng;Jiafeng Guo;Huawei Shen

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China and Graduate University of the Chinese Academy of Sciences, Beijing, P.R. China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China

  • Venue:
  • IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
  • Year:
  • 2011

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Abstract

Faceted navigation can effectively reduce user efforts of reaching targeted resources in databases, by suggesting dynamic facet values for iterative query refinement. A key issue is minimizing the navigation cost in a user query session. Conventional navigation scheme assumes that at each step, users select only one suggested value to figure out resources containing it. To make faceted navigation more flexible and effective, this paper introduces a multi-select scheme where multiple suggested values can be selected at one step, and a selected value can be used to either retain or exclude the resources containing it. Previous algorithms for cost-driven value suggestion can hardly work well under our navigation scheme. Therefore, we propose to optimize the navigation cost using the Minimum Description Length principle, which can well balance the number of navigation steps and the number of suggested values per step under our new scheme. An emperical study demonstrates that our approach is more cost-saving and efficient than state-of-the-art approaches.