Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users
P. Matuszyk, and M. Spiliopoulou. User Modeling, Adaptation, and Personalization
, volume 8538 of Lecture Notes in Computer Science, Springer International Publishing, (2014)
Neighbourhood-based collaborative filtering recommenders exploit the common ratings among users to identify a user’s most similar neighbours. It is known that decisions made on a naive computation of user similarity are unreliable, because the number of co-ratings varies strongly among users. In this paper, we formalize the notion of reliable similarity between two users and propose a method that constructs a user’s neighbourhood by selecting only those users that are reliably similar to her. Our method combines a statistical test and the notion of a baseline user. We report our results on typical benchmark datasets.