Accurate and Efficient Search Prediction Using Fuzzy Matching and Outcome Feedback

  • Christopher Shaun Wagner
  • Sahra Sedigh
  • Ali R. Hurson
Conference paper

DOI: 10.1007/978-3-642-41062-8_23

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8199)
Cite this paper as:
Wagner C.S., Sedigh S., Hurson A.R. (2013) Accurate and Efficient Search Prediction Using Fuzzy Matching and Outcome Feedback. In: Brisaboa N., Pedreira O., Zezula P. (eds) Similarity Search and Applications. SISAP 2013. Lecture Notes in Computer Science, vol 8199. Springer, Berlin, Heidelberg

Abstract

While search engines have demonstrated improvement in both speed and accuracy, the response time to queries is prohibitively long for applications that require immediate and accurate responses to search queries. Examples include identification of multimedia resources related to the subject matter of a particular class, as it is in session. This paper begins with a survey of recommendation and prediction algorithms, each of which applies a different method to predict future search activity based on the search history of a user. To address the shortcomings identified in existing techniques, we draw inspiration from bioinformatics and latent semantic indexing to propose a novel predictive approach based on local alignment and feedback-based neighborhood refinement. We validate our proposed approach with tests on real-world search data. The results support our hypothesis that a majority of users exhibit search behavior that is predictable. Modeling this behavior enables our predictive search engine to bypass the common query-response model and proactively deliver a list of resources to the user.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christopher Shaun Wagner
    • 1
  • Sahra Sedigh
    • 1
  • Ali R. Hurson
    • 1
  1. 1.Missouri University of Science and TechnologyRollaUSA

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