Local Matrix Factorization with Social Network Embedding

Jinmao Xu, Shuaiheng Peng, Daofu Gong, Fenlin Liu
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Abstract

In the recommender system, how to construct submatrices for local matrix factorization is an important problem. In this paper, we propose the Local Matrix Factorization with Social Network Embedding (LMFE) method in order to construct more meaningful sub-matrices and improve the performance of the recommender system. Firstly we utilize the user's social information and rating information to construct a heterogeneous information network (HIN). And then extract the node representations of users and items from HIN. We use the representations of the node as the basis for sub-matrix division. Finally, the local matrix factorization is performed on sub-matrix to obtain the prediction results. Experimental results from the real-world dataset Yelp demonstrate that the LMFE can achieve better performance than the comparative method.
基于社会网络嵌入的局部矩阵分解
在推荐系统中,如何构造用于局部矩阵分解的子矩阵是一个重要问题。为了构造更多有意义的子矩阵,提高推荐系统的性能,本文提出了基于社会网络嵌入的局部矩阵分解(LMFE)方法。首先利用用户的社会信息和评价信息构建异构信息网络。然后从HIN中提取用户和项目的节点表示。我们使用节点的表示作为子矩阵划分的基础。最后,对子矩阵进行局部矩阵分解,得到预测结果。来自真实数据集Yelp的实验结果表明,LMFE比比较方法取得了更好的性能。
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