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.
Recommender systems Collaborative filtering Content-based filtering Gray-sheep users Clustering
Mustansar Ali Ghazanfar Adam Prugel-Bennett
School of Electronics and Computer Science University of Southampton Highfield Campus, SO17 1BJ, Uni School of Electronics and Computer ScienceUniversity of SouthamptonHighfield Campus, SO17 1BJ, Unite
国际会议
哈尔滨
英文
403-408
2011-01-18（万方平台首次上网日期，不代表论文的发表时间）