Abstract
Most graph-based recommendation approaches actually have made an implicit assumption about that representations of all users and all items can be learned in a single latent space. However, this assumption may be too strong to well describe a single use’s multifaceted preferences each probably dominated by some latent type of motivations. This paper challenges this assumption and proposes a MultiGCN model (Multicommunity Graph Convolution Networks with Decision Fusion) to leverage multiple latent spaces for capturing multiple types of motivation. Specifically, we first design a community exploration module to construct multiple communities so as to explore different latent types of motivation. We next design a local recommendation module which maps the representations of entities in each community into one latent space and outputs a local recommendation list. A decision fusion module reranks the items of local lists to obtain the final recommendation list. Experiment results on three real-world datasets demonstrate that our MultiGCN outperforms the state-of-the-art algorithms.
Supported by National Natural Science Foundation of China (Grant No: 62172167).
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Notes
- 1.
- 2.
Douban Movie: http://www.shichuan.org/HIN_dataset.html.
- 3.
MovieLens: https://grouplens.org/datasets/movielens/.
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Liu, S., Wang, B., Liu, B., Yang, L.T. (2022). Multicommunity Graph Convolution Networks with Decision Fusion for Personalized Recommendation. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_2
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