Hoeffding-CF: Neighbourhood-Based Recommendations on Reliably Similar Users
User Modeling, Adaptation, and Personalization , volume 8538 of Lecture Notes in Computer Science, Springer International Publishing, (2014)
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.
Links and resources
- DOI:
- 10.1007/978-3-319-08786-3_13
- URL:
- https://kmd.cs.ovgu.de/pub/matuszyk/MatuszykSpiliopoulou14.pdf
- BibTeX key:
- MatuszykSpiliopoulouUmap14
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