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Home > Archive>Volume 29, Issue 2, 2018 >340-362. DOI:10.13328/j.cnki.jos.005391
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Survey of Matrix Factorization Based Recommendation Methods by Integrating Social Information
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National Natural Science Foundation of China (61370129, 61375062, 61632004)

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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.

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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

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  • Received:June 20,2017
  • Revised:July 25,2017
  • Online: October 09,2017
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