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 platform'...Show MoreMetadata
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 platform's prebuilt bundles to users; 2) generate personalized bundles for users. In this work, we propose two graph neural network models, a BGCN model (short for Bundle Graph Convolutional Network) for prebuilt bundle recommendation, and a BGGN model (short for Bundle Graph Generation Network) 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 user's 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, which outperforms the state-of-the-art baselines by 10.77% to 23.18% and 20.90% to 64.52%, respectively. We have released the datasets and codes at this link: https://github.com/cjx0525/BGCN.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 35, Issue: 3, 01 March 2023)