Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (1): 53-59.DOI: 10.11772/j.issn.1001-9081.2020060995

Special Issue: 第八届中国数据挖掘会议(CCDM 2020)

• China Conference on Data Mining 2020 (CCDM 2020) • Previous Articles     Next Articles

Collaborative filtering method fusing overlapping community regularization and implicit feedback

LI Xiangkun1,2, JIA Caiyan1,2   

  1. 1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;
    2. Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University), Beijing 100044, China
  • Received:2020-05-31 Revised:2020-09-07 Online:2021-01-10 Published:2020-10-12
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61876016, 61632004), the Fundamental Research Funds for the Central Universities (2018JBZ006).


李翔锟1,2, 贾彩燕1,2   

  1. 1. 北京交通大学 计算机与信息技术学院, 北京 100044;
    2. 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
  • 通讯作者: 李翔锟
  • 作者简介:李翔锟(1996-),男,山东济宁人,硕士研究生,主要研究方向:机器学习、推荐系统;贾彩燕(1976-),女,广西宁夏人,教授,博士,主要研究方向:数据挖掘、社会计算。
  • 基金资助:

Abstract: Aiming at the problems of data sparsity and cold start in the current recommendation system, a collaborative filtering method fusing Overlapping Community Regularization and Implicit Feedback (OCRIF) was proposed, which not only considers the community structure of users in the social network, but also integrates the implicit feedback of user rating information and social information into the recommendation model. In addition, as network representation learning can effectively learn the nodes? neighbor information on global structure of social network, a network representation learning enhanced OCRIF (OCRIF+) was proposed, which combines the low dimensional representation of users in social network with user commodity features, and can represent the similarity between the users and the membership degrees of the users to the interest communities more effectively. Experimental results on multiple real datasets show that the proposed method is superior to the similar methods on the recommendation effect. Compared with TrustSVD (Trust Support Vector Machine) method, the proposed method has the Root Mean Square Error (RMSE) decreased by 2.74%, 2.55% and 1.83% respectively, and Mean Absolute Error (MAE) decreased by 3.47%, 2.97% and 2.40% respectively on FilmTrust, DouBan and Ciao datasets.

Key words: collaborative filtering, recommendation system, social network, network embedding, overlapping community

摘要: 针对目前推荐系统存在的数据稀疏和冷启动等问题,提出了一种融合重叠社区正则化及隐式反馈的协同过滤方法(OCRIF),该方法不仅考虑了用户在社交网络中的社区结构,而且将用户评分信息与社交信息的隐式反馈融入推荐模型之中。此外,由于网络表示学习可以有效学习节点在社交网络的全局结构上的近邻信息,提出了一种网络表示学习增强的OCRIF(OCRIF+),该方法结合社交网络中用户在网络中的低维表示与用户-商品特征,能更有效地刻画用户之间的相似度及用户对兴趣社区的归属度。多个真实数据集上的实验结果显示:所提出的方法的推荐效果优于同类方法,与TrustSVD方法相比,在FilmTrust、DouBan以及Ciao数据集上,该方法的均方根误差(RMSE)分别下降了2.74%、2.55%以及1.83%,平均绝对误差(MAE)分别下降了3.47%、2.97%以及2.40%。

关键词: 协同过滤, 推荐系统, 社交网络, 网络嵌入, 重叠社区

CLC Number: