Evaluating Information Filtering Techniques in an Adaptive Recommender System

  • John O’Donovan
  • John Dunnion
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3137)


With the huge increase in the volume of information available in digital form and the increasing diversity of Web applications, the need for efficient, reliable information filtering is critical. New algorithms that filter information for specific tastes are being developed to tackle the problem of information overload. This paper proposes that there is a substantial relative difference in the performances of various filtering algorithms as they are applied to different datasets, and that these performance differences can be leveraged to form the basis of an Adaptive Information Filtering System. We classify five different datasets based on a number of metrics, including sparsity, ratings distribution and user-item ratio, and develop a regression function over these metrics to predict the suitability of a particular recommendation algorithm for a previously unseen dataset. Our results show that the predicted best algorithm does perform best on the new dataset.


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  1. 1.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proceedings of the 15th International Conference on Machine Learning, pp. 46–54. Morgan Kaufmann, San Francisco (1998)Google Scholar
  2. 2.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)zbMATHCrossRefGoogle Scholar
  3. 3.
    Hayes, C., Cunningham, P., Clerkin, P., Grimaldi, M.: Programme-driven music radio (2002)Google Scholar
  4. 4.
    Karypis Evaluation, G.: of item-based top-n recommendation algorithms. In: CIKM, pp. 247–254 (2001)Google Scholar
  5. 5.
    Melville, P., Mooney, R., Nagarajan, R.: Content-boosted collaborative filtering (2001)Google Scholar
  6. 6.
    O’Donovan, J., Dunnion, J.: A comparison of collaborative recommendation algorithms over diverse data. In: Proceedings of the National Conference on Artificial Intelligence and Cognitive Science (AICS), Ireland, September 17– September 19, pp. 101–104 (2003)Google Scholar
  7. 7.
    Raftery, A.E., Madigan, D., Hoeting Bayesian, J.A.: model averaging for linear regression models. Journal of the American Statistical Association 92(437), 179–191 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Reidl, J.: Item-based collaborative filtering recommendation algorithms. World Wide Web, 285–295 (2001)Google Scholar
  9. 9.
    Smyth, B., Wilson, D., O’Sullivan, D.: Improving the quality of the personalised electronic programme guide. In: Proceedings of TV 2002, the 2nd Workshop on Personalisation in Future TV, May 2002, pp. 42–55 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • John O’Donovan
    • 1
  • John Dunnion
    • 1
  1. 1.Intelligent Information Retrieval Group, Department of Computer ScienceUniversity College DublinDublinIreland

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