"Fulfilling the Needs of Gray-Sheep Users in Recommender Systems, A Clustering Solution"
Mustansar Ali Ghazanfar, Adam Prügel‐Bennett · ePrints Soton (University of Southampton) · 2011
Abstract—Recommender systems apply data mining tech-niques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on gray-sheep 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 algo-rithms, depending on the data sparsity and distribution; (2) gray-sheep users can be identified using clustering algorithms in off-line fashion, where the similarity threshold to isolate these users from the rest of clusters can be found empirically; (3) content-based 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. Keywords-Recommender systems; Collaborative filtering; Content-based filtering; Gray-sheep users; Clustering