Co-purchaser Recommendation for Online Group Buying
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Co‑purchaser Recommendation for Online Group Buying Jihong Chen1 · Wei Chen1 · Jinjing Huang1 · Jinhua Fang1 · Zhixu Li1 · An Liu1 · Lei Zhao1 Received: 11 March 2020 / Revised: 12 July 2020 / Accepted: 28 July 2020 / Published online: 9 August 2020 © The Author(s) 2020
Abstract 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. Keywords Group buying · Collaborator recommendation · Network embedding · Truncated walk
1 Introduction On online group buying, buyers round up some like-minded people to purchase the same products, which can leverage a large number of people’s collective bargaining power and achieve group discounts [1, 2]. In recent years, benefit from the advanced electronic payment technology and convenient express service, we have witnessed the prosperity of it in some online shopping services (e.g., TaoBao,1 Groupon,2 and PDD).3 In real applications, the co-purchase usually includes the following steps: Firstly, merchants promise to offer products or services with a discount on the condition that a certain number of customers would make the purchase. As shown in Fig. 1a, there are multiple buyers in a transaction, which
is also the essential feature of the co-purchase; then, an initiator manually invites friends, followers, and like-minded people to participate in the purchase; finally, co-purchasers accept the invitation and
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