Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution

Abstract

Recommender systems apply data mining techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on graysheep users problem responsible for the increased error rate in collaborative filtering based recommender systems algorithms. The main contribution of this paper lies in showing that (1) the presence of gray-sheep users can affect the performance— accuracy and coverage—of collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) graysheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of clusters can be found empirically; (3) contentbased profile of gray-sheep users can be used for making accurate recommendations. The effectiveness of the proposed algorithm is tested on the MovieLens dataset and community of movie fans in the FilmTrust Website, using mean absolute error, receiver operating characteristic sensitivity, and coverage.

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