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.
Highlights
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]
To address the weak similar co-purchaser problem, we propose a novel neighborhoods sampling strategy that is beneficial to the weakly similar co-purchasers, which can smoothly improve the co-occurrence probability of the weakly similar co-purchasers by truncated bias walk
We can see that the performance of cop2vec gain is more significant on TaoBao dataset, and the area under curve (AUC) score is 7% higher than PathSim diffused structural deep network embedding (PDSDNE), 11% higher than DeepWalk, and 19% higher than common neighbors
Summary
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 the above collaborator recommendation tasks, finding robustly similar users for the target user is a core task, for example, in an academic network, people tend to repetitively collaborate with fellow researchers with close researcher topics [14, 22, 23]. A large number of weak similar users participated in the copurchase transaction, but they are usually not noticed by existing recommendation methods. Cop2vec can smoothly improve the co-occurrence probability of the weak similar co-purchasers by truncated bias walk and learn a more reasonable representation for co-purchasers. – We propose PDSDNE and cop2vec, two efficient co-purchaser recommendation methods, which effectively perceive weak similar co-purchasers.
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