联合正则化的矩阵分解推荐算法

推荐系统已成为一种解决信息过载和帮助用户决策的有效工具.当前的研究表明，结合社会关系的推荐模型能够提升推荐的性能.然而，已有的社会化推荐模型大都忽略了物品之间的关联关系对推荐性能的影响.针对此问题，提出一种度量物品之间关联程度的方法，并将其用于获取物品之间的关联关系.然后，将关联关系与社会关系相结合，提出一种基于联合正则化的矩阵分解推荐模型，并证明了联合正则化是一种加权的原子范数.最后，根据提出的模型构建了一种推荐算法CRMF.在4个真实数据集上的实验结果表明：与主流的推荐算法相比，该算法不仅可以缓解用户的冷启动问题，而且更能有效地预测不同类型用户的实际评分.

Co-Regularized Matrix Factorization Recommendation Algorithm

Recommender systems have been successfully adopted as an effective tool to alleviate information overload and assist users to make decisions. Recently, it has been demonstrated that incorporating social relationships into recommender models can enhance recommendation performance. Despite its remarkable progress, a majority of social recommendation models have overlooked the item relations-a key factor that can also significantly influence recommendation performance. In this paper, a approach is first proposed to acquire item relations by measuring correlations among items. Then, a co-regularized recommendation model is put forward to integrate the item relations with social relationships by introducing co-regularization term in the matrix factorization model. Meanwhile, that the co-regularization term is a case of weighted atomic norm is illustrated. Finally, based on the proposed model a recommendation algorithm named CRMF is constructed. CRMF is compared with existing state-of-the-art recommendation algorithms based on the evaluations over four real-world data sets. The experimental results demonstrate that CRMF is able to not only effectively alleviate the user cold-start problem, but also help obtain more accurate rating predictions of various users.