融合重叠社区正则化及隐式反馈的协同过滤方法  

Collaborative filtering method fusing overlapping community regularization and implicit feedback

作　　者：李翔锟 贾彩燕[1,2] LI Xiangkun;JIA Caiyan(School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100044,China;Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University),Beijing 100044,China)

机构地区：[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

出　　处：《计算机应用》2021年第1期53-59,共7页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61876016,61632004);中央高校基本科研业务费专项资金资助项目(2018JBZ006)。

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%。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 nodess'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.

关 键 词：协同过滤 推荐系统 社交网络 网络嵌入 重叠社区 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]