The effect of correlation coefficients on communities of recommenders

ABSTRACT

Recommendation systems, based on collaborative filtering, offer a means of sifting through the enourmous amounts of content on the web by composing user ratings in order to generate predicted ratings for other users. These kinds of systems can be viewed as a network of interacting peers, where each user is a node and the links to all other nodes are weighted according to how similar the corresponding users are. Predicted ratings are generated for a user for unknown items by requesting and aggregating rating information from the surrounding neighbors. However, the different methods of computing user similarity, or weighting the network links, very often do not agree with each other, and, as a result, the structure of the network of recommenders changes completely. In this work we perform an analysis of a range of similarity measures, comparing their performance in terms of prediction accuracy and coverage. This allows us to understand the effect that similarity measures have on predicted ratings. Based on the obtained results, we argue that user-similarity may not sufficiently capture the relationships that recommenders could otherwise share in order to maximise the utility of these communities.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

Keywords:
collaborative filtering, correlation, recommender systems