Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations

  • Linas Baltrunas
  • Francesco Ricci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5149)

Abstract

User-to-user similarity is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user similarity the ratings assigned by two users to a set of items are pairwise compared and averaged (correlation). In this paper we make user-to-user similarity adaptive, i.e., we dynamically change the computation depending on the profiles of the compared users and the target item whose rating prediction is sought. We propose to base the similarity between two users only on the subset of co-rated items which best describes the taste of the users with respect to the target item. These are the items which have the highest correlation with the target item. We have evaluated the proposed method using a range of error measures and showed that the proposed locally adaptive neighbor selection, via item selection, can significantly improve the recommendation accuracy compared to standard CF.

Keywords

Recommender System Target Item Rating Prediction Target User Collaborative Filter 
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 2008

Authors and Affiliations

  • Linas Baltrunas
    • 1
  • Francesco Ricci
    • 1
  1. 1.Free University of Bozen-BolzanoBozenItaly

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