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Query Ambiguity Identification Based on User Behavior Information

  • Cheng Luo
  • Yiqun Liu
  • Min Zhang
  • Shaoping Ma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8870)

Abstract

Query ambiguity identification is of vital importance for Web search related studies such as personalized search or diversified ranking. Different from existing solutions which usually require a supervised topic classification process, we propose a query ambiguity identification framework which takes user behavior features collected from click-through logs into consideration. Especially, besides the features collected from query level, we focus on how to tell the differences between clear and ambiguous queries via features extracted from multi-query sessions. Inspired by recent progresses in word representation researches, we propose a query representation approach named “query2vec” which constructs representations from the distributions of queries in query log context. Experiment results based on large scale commercial search engine logs show effectiveness of the proposed framework as well as the corresponding representation approach.

Keywords

Search Engine User Session Personalized Search Query Intent Query Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cheng Luo
    • 1
  • Yiqun Liu
    • 1
  • Min Zhang
    • 1
  • Shaoping Ma
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems,Tsinghua National Laboratory for Information Science and Technology,Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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