Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Recommender Systems Handbook
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Recommendation systems provide us a promising approach to deal with the information overload problem. Collaborative filtering is the key technology in these systems. In the past decades, model-based and memory-based methods have been the main research areas of collaborative filtering. Empirically, model-based methods may achieve higher prediction accuracy than memory-based methods. On the other side, memory-based methods (e.g. slope one algorithm) provide a concise and intuitive justification for the computed predictions. In order to take advantages of both model-based and memory-based methods, we propose a new approach by introducing the idea of machine learning to slope one algorithm. Several strategies are presented in this paper to catch this goal. Experiments on the MovieLens dataset show that our approach achieves great improvement of prediction accuracy.