ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce

Jun Wu, Yuanyuan Li, Li Shi, Liping Yang, Xiaxia Niu and Wen Zhang
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Jun Wu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Yuanyuan Li: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Li Shi: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Liping Yang: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Xiaxia Niu: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Wen Zhang: College of Economics and Management, Beijing University of Technology, Beijing 100124, China

Mathematics, 2022, vol. 10, issue 2, 1-20

Abstract: Existing studies have made a great endeavor in predicting users’ potential interests in items by modeling user preferences and item characteristics. As an important indicator of users’ satisfaction and loyalty, repeat purchase behavior is a promising perspective to extract insightful information for community e-commerce. However, the repeated purchase behaviors of users have not yet been thoroughly studied. To fill in this research gap from the perspective of repeated purchase behavior and improve the process of generation of candidate recommended items this research proposed a novel approach called ReRec (Repeat purchase Recommender) for real-life applications. Specifically, the proposed ReRec approach comprises two components: the first is to model the repeat purchase behaviors of different types of users and the second is to recommend items to users based on their repeat purchase behaviors of different types. The extensive experiments are conducted on a real dataset collected from a community e-commerce platform, and the performance of our model has improved at least about 13.6% compared with the state-of-the-art techniques in recommending online items (measured by F-measure). Specifically, for active users, with w = 1 and N U A ∈ 5 , 25 , the results of ReRec show a significant improvement (at least 50%) in recommendation. With α and σ as 0.75 and 0.2284, respectively, the proposed ReRec for unactive users is also superior to (at least 13.6%) the evaluation indicators of traditional Item CF when N U B ∈ 6 , 25 . To the best of our knowledge, this paper is the first to study recommendations in community e-commerce.

Keywords: ReRec; community e-commerce; repeat purchase; user behavior modeling; recommendation system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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