Artificial Intelligence Review

, Volume 21, Issue 3–4, pp 193–213 | Cite as

An Accurate and Scalable Collaborative Recommender

  • Jerome Kelleher
  • Derek Bridge

Abstract

We present a collaborative recommender that uses a user-based model to predict user ratings for specified items. The model comprises summary rating information derived from a hierarchical clustering of the users. We compare our algorithm with several others. We show that its accuracy is good and its coverage is maximal. We also show that the algorithm is very efficient: predictions can be made in time that grows independently of the number of ratings and items and only logarithmically in the number of users.

clustering collaborative filtering recommender systems 

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Jerome Kelleher
  • Derek Bridge

There are no affiliations available

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