GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative filtering recommender systems make predictions based on the preferences of users considered like-minded to the target user (user-based), or the popularities of items similar to the target item (item-based). There have been several approaches of combining user-based and item-based collaborative filtering. However, they are predominantly along the lines of averaging user-based and item-based predictions in a close-to-linear fashion, thus behave like smoothing mechanisms and only work well on sparse datasets. This article proposes a new way of combining user and item based collaborative filtering in a nonlinear fashion. The goal of the approach is to improve recommendation accuracy on regular datasets, by means of a more sensible neighbourhood similarity computation method that guides the user similarity computation using the items’ similarities to the item that is being predicted.