Query Click and Text Similarity Graph for Query Suggestions

  • D. Sejal
  • K. G. Shailesh
  • V. Tejaswi
  • Dinesh Anvekar
  • K. R. Venugopal
  • S. S. Iyengar
  • L. M. Patnaik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9166)

Abstract

Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users’ need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion.

Keywords

Image suggestion Query suggestion Query relevance Recommendation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • D. Sejal
    • 1
  • K. G. Shailesh
    • 1
  • V. Tejaswi
    • 2
  • Dinesh Anvekar
    • 3
  • K. R. Venugopal
    • 1
  • S. S. Iyengar
    • 4
  • L. M. Patnaik
    • 5
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of Engineering, Bangalore UniversityBangalore-1India
  2. 2.National Institute of TechnologySurathkalIndia
  3. 3.Nitte Meenakshi Institute of TechnologyBangaloreIndia
  4. 4.Florida International UniversityMiamiUSA
  5. 5.Indian Institute of ScienceBangaloreIndia

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