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Heterogeneous information network-based interest composition with graph neural network for recommendation

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Abstract

Heterogeneous information networks (HINs) are widely applied to recommendation systems due to their capability of modeling various auxiliary information with meta-paths. However, existing HIN-based recommendation models usually fuse the information from various meta-paths by simple weighted sum or concatenation, which limits performance improvement because it lacks the capability of interest compositions among meta-paths. In this article, we propose an HIN-based Interest Composition model for Recommendation (HicRec). Specifically, user and item representations are learned with a graph neural network on both the graph structure and features in each meta-path, and a parameter sharing mechanism is utilized here to ensure that the user and item representations are in the same latent space. Then, users’ interests in each item from each pair of related meta-paths are calculated by a combination of the user and item representations. The composed user interests are obtained by their single interest from both intra- and inter-meta-paths for recommendation. Extensive experiments are conducted on three real-world datasets and the results demonstrate that our proposed HicRec model outperforms the baselines.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No.61872002, U1936220), the University Natural Science Research Project of Anhui Province (Grant No. KJ2019A0037), and the Natural Science Foundation of Anhui Province of China (No.1808085MF197). Yiwen Zhang is the corresponding author of this paper.

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  1. School of Computer Science and Technology, Anhui University, Hefei, China

    Dengcheng Yan, Wenxin Xie & Yiwen Zhang

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  1. Dengcheng Yan
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  2. Wenxin Xie
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Correspondence to Yiwen Zhang.

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Yan, D., Xie, W. & Zhang, Y. Heterogeneous information network-based interest composition with graph neural network for recommendation. Appl Intell 52, 11199–11213 (2022). https://doi.org/10.1007/s10489-021-03018-6

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