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What Have the Neighbours Ever Done for Us? A Collaborative Filtering Perspective

  • Rachael Rafter
  • Michael P. O’Mahony
  • Neil J. Hurley
  • Barry Smyth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5535)

Abstract

Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems. Common approaches such as user-based and item-based methods generate predictions from the past ratings of users by combining two separate ratings components: a base estimate, generally based on the average rating of the target user or item, and a neighbourhood estimate, generally based on the ratings of similar users or items. The common assumption is that the neighbourhood estimate gives CF techniques a considerable edge over simpler average-rating techniques. In this paper we examine this assumption more carefully and demonstrate that the influence of neighbours can be surprisingly minor in CF algorithms, and we show how this has been disguised by traditional approaches to evaluation, which, we argue, have limited progress in the field.

Keywords

Recommender Systems Collaborative Filtering Predictive Accuracy 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rachael Rafter
    • 1
  • Michael P. O’Mahony
    • 1
  • Neil J. Hurley
    • 1
  • Barry Smyth
    • 1
  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College Dublin, BelfieldDublin 4Ireland

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