Bundle Recommendation and Generation with Graph Neural Networks
Jianxin Chang, Chen Gao, Xiangnan He, Depeng Jin, Yong Li
Abstract
Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Related work can be divided into two categories: 1) to recommend the platforms prebuilt bundles to users; 2) generate personalized bundles for users. These two problems are not well solved. In this work, we propose two graph neural network models, a BGCN model for prebuilt bundle recommendation, and a BGGN model for personalized bundle generation. First, BGCN unifies the user-item interaction, the user-bundle interaction and the bundle-item affiliation into a heterogeneous graph. With item nodes as the bridge, graph convolutional propagation between user and bundle nodes makes the learned representations capture the item-level semantics. Second, BGGN re-constructs bundles into graphs based on the item co-occurrence pattern and the users supervision signal. The complex and high-order item-item relationships in the bundle graph are explicitly modeled through graph generation. Empirical results demonstrate the substantial performance gains of BGCN and BGGN. We have released the datasets and codes at this link: https://github.com/cjx0525/BGCN.