Accurate and Efficient Search Prediction Using Fuzzy Matching and Outcome Feedback

  • Christopher Shaun Wagner
  • Sahra Sedigh
  • Ali R. Hurson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8199)

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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References

  1. 1.
    Levene, M.: An Introduction to Search Engines and Web Navigation, 2nd edn. John Wiley & Sons (2010)Google Scholar
  2. 2.
    Mostafa, J.: Seeking better web searches. Scientific American 292(2), 66–73 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hawking, D., Craswell, N., Brailey, P., Griffihs, K.: Measuring search engine quality. Information Retrieval 4(1), 33–59 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Wickelgren, W.A.: Speed-accuracy tradeoff and information processing dynamics. Acta Psychologica 41(1), 67–85 (1977)CrossRefGoogle Scholar
  5. 5.
    Konstan, J.A., Riedl, J.: Recommender systems: From algorithms to user experience. User Modeling and User-Adapted Interaction 22, 101–123 (2012)CrossRefGoogle Scholar
  6. 6.
    Cleger-Tamayo, S., Fernández-Luna, J.M., Huete, J.F., Pérez-Vázquez, R., Rodríguez Cano, J.C.: A proposal for news recommendation based on clustering techniques. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds.) IEA/AIE 2010, Part III. LNCS, vol. 6098, pp. 478–487. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Tellez, E.S., Chavez, E., Navarro, G.: Succinct nearest neighbor search. In: Proceedings of the 4th International Conference on Similarity Search and Applications (SISAP), pp. 33–40 (June 2011)Google Scholar
  8. 8.
    Zipf, G.K.: The psycho-biology of language. Language 12, 196–210 (1936)CrossRefGoogle Scholar
  9. 9.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)MATHCrossRefGoogle Scholar
  10. 10.
    Xiong, L., Xiang, Y., Zhang, Q., Lin, L.: A novel nearest neighborhood algorithm for recommender systems. In: Proceedings of the Third Global Congress on Intelligent Systems(GCIS), pp. 156–159 ( November 2012)Google Scholar
  11. 11.
    Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos (1991)Google Scholar
  12. 12.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn. Pearson Addison-Wesley (2005)Google Scholar
  13. 13.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin del la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)MATHGoogle Scholar
  14. 14.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady 10, 707–710 (1966)MathSciNetGoogle Scholar
  15. 15.
    Bertsekas, D.P.: Dynamic Programming and Optimal Control, 3rd edn. Athena Scientific (2007)Google Scholar
  16. 16.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms, 3rd edn. The MIT Press (2009)Google Scholar
  17. 17.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. Journal of the ACM 21(1), 168–173 (1974)MathSciNetMATHCrossRefGoogle Scholar
  18. 18.
    Kolmogorov, A.N.: On tables of random numbers. Theoretical Computer Science 207, 387–395 (1963)MathSciNetCrossRefGoogle Scholar

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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