联合聚类和评分矩阵共享的协同过滤推荐

Collaborative Filtering Recommendation with Co-clustering and Rating-Matrix Sharing

针对传统协同过滤推荐(collaborative filtering recommendation,CFR)受数据聚类预处理,评分矩阵稀疏性影响较大和多个评分矩阵之间不能知识迁移的问题,提出了一种基于联合聚类和评分矩阵共享的协同过滤推荐方法,以提高推荐系统精度和泛化能力。该方法首先通过联合聚类对原始评分矩阵进行用户和项目两个维度的聚类;然后对评分矩阵进行分解并取得共享组级评分矩阵;最后利用共享组级评分矩阵和迁移学习方法进行评分预测。对MovieLents和Book-Crossing两个数据集进行了仿真实验,结果表明该方法相比传统方法平均绝对误差减少近8%,有效地提高了协同过滤推荐的预测精度,为协同过滤推荐的应用提供借鉴。

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.