###
Journal of Software:2018.29(2):340-362

融合社交信息的矩阵分解推荐方法研究综述
刘华锋,景丽萍,于剑
(交通数据分析与挖掘北京市重点实验室(北京交通大学), 北京 100044;北京交通大学 计算机与信息技术学院, 北京 100044)
Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information
LIU Hua-Feng,JING Li-Ping,YU Jian
(Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University), Beijing 100044, China;School of Computer Science and Technology, Beijing JiaoTong University, Beijing 100044, China)
Abstract
Chart / table
Reference
Similar Articles
Article :Browse 2999   Download 3437
Received:June 20, 2017    Revised:July 25, 2017
> 中文摘要: 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.
Abstract: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.
文章编号:     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61370129,61375062,61632004) 国家自然科学基金(61370129,61375062,61632004)
Foundation items:National Natural Science Foundation of China (61370129, 61375062, 61632004)
Reference text:

刘华锋,景丽萍,于剑.融合社交信息的矩阵分解推荐方法研究综述.软件学报,2018,29(2):340-362

LIU Hua-Feng,JING Li-Ping,YU Jian.Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information.Journal of Software,2018,29(2):340-362