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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.

Original publication

DOI

10.1023/B:AIRE.0000036255.53433.26

Type

Conference paper

Publication Date

01/06/2004

Volume

21

Pages

193 - 213