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A Framework for Evaluation of Information Filtering Techniques in an Adaptive Recommender System

  • John O’Donovan
  • John Dunnion
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2945)

Abstract

This paper proposes that there is a substantial relative difference in the performance of information-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 metrics such as sparsity, user-item ratio etc, and develop a regression function over these metrics in order to predict suitability of a particular recommendation algorithm to a new dataset, using only the aforementioned metrics. Our results show that the predicted best algorithm does perform better for the new dataset.

Keywords

Recommender System Regression Function Collaborative Filter Bayesian Model Aver Recommendation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • John O’Donovan
    • 1
  • John Dunnion
    • 1
  1. 1.Department of Computer ScienceUniversity College DublinIreland

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