关键词: 推荐系统, 协同过滤, 大数据, 稀疏矩阵, 联合聚类, 迁移学习

Abstract: In view that the traditional collaborative filtering recommendation (CFR) is affected largely by the data clustering preprocess and the sparsity of rating-matrix, cannot allow knowledge-sharing across multiple rating matrices, this paper puts forward a new collaborative filtering method of combining the co-clustering with rating-matrix sharing to improve the forecasting accuracy and generalization ability. Firstly, it uses the co-clustering method to divide the raw rating-matrix into clusters by two dimensions of users and items. Secondly, it factorizes the rating-matrix and obtains a cluster-level rating-matrix. At last, it predicts the rating-matrix using the cluster-level rating-matrix and transfer learning method. A simulation experiment using databases of MovieLents and Book-Crossing is carried out. The results show that this method reduces nearly 8% for the average absolute error value compared to the traditional CFR, and improves the prediction accuracy of recommender system. This method provides references for the collaborative filtering recommendation.

Key words: recommender system, collaborative filtering, big data, sparse matrix, co-clustering, transfer learning