Bundle Recommendation with Graph Convolutional Networks

• URL: http://arxiv.org/abs/2005.03475v1
• Date: Thu, 7 May 2020 13:48:26 GMT
• Title: Bundle Recommendation with Graph Convolutional Networks
• Authors: Jianxin Chang, Chen Gao, Xiangnan He, Yong Li, Depeng Jin
• Abstract summary: Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner. We propose a graph neural network model named BGCN (short for textittextBFBundle textBFGraph textBFConvolutional textBFNetwork) for bundle recommendation.
• Score: 71.95344006365914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bundle recommendation aims to recommend a bundle of items for a user to consume as a whole. Existing solutions integrate user-item interaction modeling into bundle recommendation by sharing model parameters or learning in a multi-task manner, which cannot explicitly model the affiliation between items and bundles, and fail to explore the decision-making when a user chooses bundles. In this work, we propose a graph neural network model named BGCN (short for \textit{\textBF{B}undle \textBF{G}raph \textBF{C}onvolutional \textBF{N}etwork}) for bundle recommendation. BGCN unifies user-item interaction, user-bundle interaction and 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. Through training based on hard-negative sampler, the user's fine-grained preferences for similar bundles are further distinguished. Empirical results on two real-world datasets demonstrate the strong performance gains of BGCN, which outperforms the state-of-the-art baselines by 10.77\% to 23.18\%.

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