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Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network

School of Economics and Management, Beihang University, Beijing 100191, China
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Information 2020, 11(3), 171; https://doi.org/10.3390/info11030171 (registering DOI)
Received: 24 February 2020 / Revised: 20 March 2020 / Accepted: 21 March 2020 / Published: 23 March 2020
(This article belongs to the Section Information Systems)
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. View Full-Text
Keywords: recommender systems; customer loyalty; complex networks recommender systems; customer loyalty; complex networks
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Bai, Y.; Jia, S.; Wang, S.; Tan, B. Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network. Information 2020, 11, 171.

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