摘要
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.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,分析了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展望.
With the increasing of social network, social recommendation becomes hot research topic in recommendation systems. Matrix factorization based（MF-based） recommendation model gradually becomes the key component of social recommendation due to its high expansibility and flexibility. Thus, this paper focuses on MF-based social recommendation methods. Firstly, it reviews the existing social recommendation models according to the model construction strategies. Next, it conducts a series of experiments on real-world datasets to demonstrate the performance of different social recommendation methods from three perspectives including whole-users, cold start-users, and long-tail items. Finally, the paper analyzes the problems of MF-based social recommendation model, and discusses the possible future research directions and development trends in this research area.
出处
《软件学报》
EI
CSCD
北大核心
2018年第2期340-362,共23页
Journal of Software
基金
国家自然科学基金(61370129
61375062
61632004)~~
关键词
推荐系统
矩阵分解
社交推荐
社交网络
协同过滤
recommendation system
matrix factorization
social recommendation
social network
collaborative filtering
