Large-scale Comb-K Recommendation

Houye Ji, Junxiong Zhu, Chuan Shi, Xiao Wang, Bai Wang, Chaoyu Zhang, Zixuan Zhu, Feng Zhang, Yanghua Li

Abstract: Promotion recommendation, as a new recommendation paradigm in recent years, plays an important role in stimulating the purchase desire of users and maximizing the total revenue. Different from previous recommendations (e.g., item/group recommendation), promotion recommendation aims to select a set of K items based on all user preferences in selection phase and maximize the total revenue in delivery phase. Although these two phases are closely related with each other, existing methods usually focus on item selection in selection phase, largely ignoring the delivery phase and leading to sub-optimal performance. To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. When selecting K items, the comb-K recommendation is able to simultaneously search the optimal combination of item selection and delivery with the full consideration of all user preferences. Specifically, we propose a novel heterogeneous graph convolutional network to estimate user preference and propose the user-level comb-K recommendation model through solving a binary combination optimization problem. In order to handle combination explosion for large-scale users, we furtherly cluster massive users into limited groups and present a group-level comb-K recommendation model in which a novel heterogeneous graph pooling network is proposed to perform user clustering and estimate group preference. In addition, considering the ”long tail” phenomenon in e-commerce, we design a restricted neighbor heuristic search to accelerate the solving process. Extensive experiments on four datasets demonstrate the superiority of comb-K model for large-scale promotion recommendation. On billion-scale data, when clustering 2.5 × 107 users into 103 groups, our model is able to preserve 98.7% personalized preferences in group-level and significantly improves the Total Click and Hit Ratio by 9.35% and 7.14%, respectively.