—In the area of recommender systems, user-based
collaborative filtering algorithm has been extensively studied and
discussed. In the traditional approach of this method, a target
user’s preference for an item is predicted by the integrated
preference of the user’s neighbors for the item, ignoring the
structure of these neighbors. That is, these neighbors form two
distinct groups: some neighbors may like the target item or give
high rating; on the other hand, some neighbors may dislike the
target item or give low rating. The structure of the two groups
may influence user’s choice. As an extension of user-based
collaborative filtering, this paper focuses on the analysis of such
structure by mining latent attributes of users’ neighborhood, and
corresponding correlations with users’ preference by several
popular data mining techniques. Mining latent attributes and
experiment evaluation was conducted on MovieLens data set.
The experimental results reveal that the proposed method can
improve the performance of pure user-based collaborative
filtering algorithm.
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