A Performance Prediction Approach to Enhance Collaborative Filtering Performance

  • Alejandro Bellogín
  • Pablo Castells
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5993)

Abstract

Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the area of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to predict neighbor performance in CF. A predictor is proposed and introduced in a kNN CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. The properties of the predictor are empirically studied by, first, checking the correlation of the predictor output with a proposed measure of neighbor performance. Then, the performance of the dynamic kNN variant is examined on different sparsity and neighborhood size conditions, where the variant consistently outperforms the baseline algorithm, with increasing difference on small neighborhoods.

Keywords

Recommender systems collaborative filtering neighbor selection performance prediction query clarity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Amati, G., Carpineto, C., Romano, G.: Query difficulty, robustness, and selective application of query expansion. In: McDonald, S., Tait, J.I. (eds.) ECIR 2004. LNCS, vol. 2997, pp. 127–137. Springer, Heidelberg (2004)Google Scholar
  3. 3.
    Aslam, J.A., Pavlu, V.: Query hardness estimation using Jensen-Shannon divergence among multiple scoring functions. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECiR 2007. LNCS, vol. 4425, pp. 198–209. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Baltrunas, L., Ricci, F.: Locally adaptive neighborhood selection for collaborative filtering recommendations. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 22–31. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Carmel, D., Yom-Tov, E., Darlow, A., Pelleg, D.: What makes a query difficult? In: 29th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2006), pp. 390–397. ACM Press, New York (2006)CrossRefGoogle Scholar
  6. 6.
    Cronen-Townsend, S., Zhou, Y., Croft, W.: Precision prediction based on ranked list coherence. Information Retrieval 9(6), 723–755 (2006)CrossRefGoogle Scholar
  7. 7.
    Cronen-Townsend, S., Zhou, Y., Croft, B.W.: Predicting query performance. In: 25th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2002), pp. 299–306. ACM Press, New York (2002)CrossRefGoogle Scholar
  8. 8.
    Diaz, F., Jones, R.: Using temporal profiles of queries for precision prediction. In: 27th annual international conference on Research and development in information retrieval (SIGIR 2004), pp. 18–24. ACM Press, New York (2004)Google Scholar
  9. 9.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Hauff, C., Azzopardi, L., Hiemstra, D.: The combination and evaluation of query performance prediction methods. In: Boughanem, M., et al. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 301–312. Springer, Heidelberg (2009)Google Scholar
  11. 11.
    He, B., Ounis, I.: Inferring query performance using pre-retrieval predictors. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 43–54. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval 5(4), 287–310 (2002)CrossRefGoogle Scholar
  13. 13.
    Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation 28(1), 11–20 (1972)CrossRefGoogle Scholar
  14. 14.
    Kwon, K., Cho, J., Park, Y.: Multidimensional credibility model for neighbor selection in collaborative recommendation. Expert Systems with Applications 36(3), 7114–7122 (2009)CrossRefGoogle Scholar
  15. 15.
    Lavrenko, V., Allan, J., Deguzman, E., Laflamme, D., Pollard, V., Thomas, S.: Relevance models for topic detection and tracking. In: 2nd int. conference on Human Language Technology Research, pp. 115–121. Morgan Kaufmann Publishers, San Francisco (2002)Google Scholar
  16. 16.
    Macdonald, C., He, B., Ounis, I.: Predicting query performance in intranet search. In: ACM SIGIR Workshop on Predicting Query Difficulty – Methods and Applications (2005)Google Scholar
  17. 17.
    Mothe, J., Tanguy, L.: Linguistic features to predict query difficulty. In: ACM SIGIR Workshop on Predicting Query Difficulty – Methods and Applications, Salvador, Brazil (2005)Google Scholar
  18. 18.
    Plachouras, V., He, B., Ounis, I.: University of Glasgow at TREC2004: Experiments in Web, Robust and Terabyte tracks with Terrier. In: 13th Text Retrieval Conference (TREC 2004), Gaithesburg, Maryland (2004)Google Scholar
  19. 19.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: 2005 International Conference on Intelligent User Interfaces (IUI), pp. 167–174. ACM Press, New York (2005)CrossRefGoogle Scholar
  20. 20.
    Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: 29th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2006), pp. 501–508. ACM Press, New York (2006)CrossRefGoogle Scholar
  21. 21.
    Xue, G.R., Lin, C., Yang, Q., Xi, W., Zeng, H.J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: 28th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2005), pp. 114–121. ACM Press, New York (2005)CrossRefGoogle Scholar
  22. 22.
    Yom-Tov, E., Fine, S., Carmel, D., Darlow, A.: Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval. In: 28th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2005), pp. 512–519. ACM Press, New York (2005)CrossRefGoogle Scholar
  23. 23.
    Zhou, Y., Croft, B.W.: Ranking robustness: a novel framework to predict query performance. In: 15th ACM conference on Information and knowledge management (CIKM 2006), pp. 567–574. ACM Press, New York (2006)CrossRefGoogle Scholar
  24. 24.
    Zhou, Y., Croft, B.W.: Query performance prediction in web search environments. In: 30th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2007), pp. 543–550. ACM Press, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alejandro Bellogín
    • 1
  • Pablo Castells
    • 1
  1. 1.Escuela Politécnica SuperiorUniversidad Autónoma de MadridMadridSpain

Personalised recommendations