Co-purchaser Recommendation for Online Group Buying

• Published in: Data science and engineering 2020-09, Vol.5 (3), p.280-292
• Main Authors: Chen, Jihong, Chen, Wei, Huang, Jinjing, Fang, Jinhua, Li, Zhixu, Liu, An, Zhao, Lei
• Format: Article
• Language: English
• Subjects: Algorithm Analysis and Problem Complexity; Artificial Intelligence; Business; Buying; Chemistry and Earth Sciences; Collaborator recommendation; Computer Science; Data Mining and Knowledge Discovery; Database Management; Group buying; Initiators; Network embedding; Physics; Purchasing; Recommender systems; Similarity; Statistics for Engineering; Systems and Data Security; Truncated walk
• ISSN: 2364-1185; 2364-1541
• DOI: 10.1007/s41019-020-00138-w

Online group buying is a burgeoning business model of Internet shopping, in which people with the same merchandise interests form a group and co-purchase goods with favorable prices. The buyer who launches the co-purchase is called the initiator, and other buyers are called the co-purchasers. Although recommending co-purchasers for a target buyer (co-purchase initiator) on the group buying is an interesting problem, existing studies have paid few attention to this topic. Different from the collaborator recommendation that only considers users with high similarity to the target user, co-purchaser recommendation takes both users with high and weak similarity into account, and the recommendation results can achieve high recall and diversity. However, the task turns out to be a challenging problem since it is hard to make a precise recommendation for buyers with weak similarity. To address the problem, we propose the following two methods. In the first one, we directly impose a penalty to the weak similar co-purchasers in the embedding space. To further improve the recommendation performance, in the second one, we smoothly increase the co-occurrence probability of the weak similar co-purchasers by truncated bias walk. Our experimental results on real datasets show that the proposed methods, particularly the latter, can effectively complete the co-purchaser recommendation and has high recommendation performance. In addition, considering that co-purchase may last longer, the total recommendation result can be generated in multiple stages and adjust the current recommendation list based on the feedback from the recommendation of previous stages. It is a trick for all co-purchaser recommendation methods to make the total result better.