Abstract
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.
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Matuszyk, P., Spiliopoulou, M. (2014). Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_13
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DOI: https://doi.org/10.1007/978-3-319-08786-3_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08785-6
Online ISBN: 978-3-319-08786-3
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