融合社交信息的矩阵分解推荐方法研究综述

随着社交网络的发展，融合社交信息的推荐成为推荐领域中的一个研究热点.基于矩阵分解的协同过滤推荐方法（简称为矩阵分解推荐方法）因其算法可扩展性好及灵活性高等诸多特点，成为研究人员在其基础之上进行社交推荐模型构建的重要原因.本文围绕基于矩阵分解的社交推荐模型，依据模型的构建方式对社交推荐模型进行综述.在实际数据上对已有代表性社交推荐方法进行对比，分析各种典型社交推荐模型在不同视角下的性能（如整体用户、冷启动用户、长尾物品）.最后，分析基于矩阵分解的社交推荐模型及其求解算法存在的问题，并对未来研究方向与发展趋势进行了展望.

Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information

With the increasing of social network, social recommendation becomes hot research topic inrecommendation systems. Matrix factorization(MF) based recommendation model gradually becomes the key component of social recommendation due to its high expansibility and flexibility.Thus, in this paper, we focus on MF-based social recommendation methods. Firstly, we review the existing social recommendation models according to the model construction strategies. Furthermore, a series of experiments on real-world datasets were conducted to demonstrate the performance of different social recommendation methods fromthree views including whole-users, cold start-users, and long-tail items. Finally, we analyze the problems ofMF-based social recommendation model, and discuss the possible future research directionsand development trends in this research area.