| 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. |
| 关键词: 推荐系统 矩阵分解 社交推荐 社交网络 协同过滤 |
| DOI：10.13328/j.cnki.jos.005391 |
| 分类号: |
| 基金项目:国家自然科学基金（61370129，61375062，61632004） |
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| Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information |
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LIU Hua-Feng1,2, JING Li-Ping1,2, YU Jian1,2
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1.Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University), Beijing 100044, China;2.School of Computer Science and Technology, Beijing JiaoTong University, Beijing 100044, China
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| 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. |
| Key words: recommendation system matrix factorization social recommendation social network collaborative filtering |
