耦合辅助信息的矩阵分解推荐模型

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》

蒋伟 秦志光

Abstract： Collaborative filtering (CF) recommender systems have been used to provide users with personalized products and services successfully in the past decade. However, sparseness of user-item matrix and the low accuracy still remain a challenge. To solve these problems, this paper proposed an ensemble framework based on matrix factorization CF for integrating side information of users and items. Based on this framework, this paper proposed a hybrid CF model for integrating COS (Coupled Object Similarity) of attribute information of items. Extensive experiments conduct over large-scale real-word datasets demonstrate that the proposed approach can effectively solve the problem of item similarity measurement, and compared with the traditional approaches, especially in the case of very sparse feature, the accuracy of the recommendation is improved effectively.

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