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Collaborative filtering algorithms predict the preferences of a user for an item by weighting the contributions of <em>similar</em>users, called <em>neighbors</em>, for that item. Similarity between users is computed by comparing their rating styles, ...

Recommender systems produce social networks as a side effect of predicting what users will like. However, the potential for these social networks to aid in recommending items is largely ignored. We propose a recommender system that works directly with ...

In this paper, we describe and discuss the evaluation process and results of a content-based music retrieval system that we have developed. In our system, user models embody the ability of evolving and using different music similarity measures for ...