期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2016
卷号：2016
DOI：10.1155/2016/9564293
出版社：Hindawi Publishing Corporation
摘要：The sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which includes user relationship network, item similarity network, and user-item relationship network, is constructed using comprehensive data among the user-item matrix and the social networks. Based on this framework, a Regression Model Recommendation Approach (RMRA) is established to calculate the correlation score between the test user and test item. The correlation score is used to predict the test user preference for the test item. The RMRA mines the potential information among both social networks and user-item matrix to improve the recommendation accuracy and ease the cold-start problem. We conduct experiment based on KDD 2012 real data set. The result indicates that our algorithm performs superiorly compared to traditional collaborative filtering algorithm.
