文章基本信息

标题：Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network
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作者：Yun Bai ; Suling Jia ; Shuangzhe Wang 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2020
卷号：11
期号：3
页码：171-186
DOI：10.3390/info11030171
出版社：MDPI Publishing
摘要：Inferring customers’ preferences and recommending suitable products is a challenging task for companies, although recommender systems are constantly evolving. Loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. To this end, the aim of this study is to explore whether customer loyalty can improve the accuracy of the recommender system. Two algorithms based on complex networks are proposed: a recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and a recommendation algorithm based on single vertex set network and DeepWalk (SVDW). In both algorithms, loyalty is taken as an attribute of the customer, and the relationship between customers and products is abstracted into the network topology. During the random walk among nodes in the network, product recommendations for customers are completed. Taking a real estate group in Malaysia as an example, the experimental results verify that customer loyalty can indeed improve the accuracy of the recommender system. We can also conclude that companies are more effective at recommending customers with moderate loyalty levels.
关键词：recommender systems; customer loyalty; complex networks recommender systems ; customer loyalty ; complex networks