The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Hierarchical Gating Networks for Sequential Recommendation
Open accessAbstract
The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed. To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. Our HGN consists of a feature gating module, an instance gating module, and an item-item product module. In particular, our feature gating and instance gating modules select what item features can be passed to the downstream layers from the feature and instance levels, respectively. Our item-item product module explicitly captures the item relations between the items that users accessed in the past and those items users will access in the future. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on five real-world datasets. The experimental results demonstrate the effectiveness of our model on Top-N sequential recommendation.
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Cites
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Self-Attentive Sequential Recommendation
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Cited by
Geng, S., Liu, S., Fu, Z., Ge, Y., & Zhang, Y. (2022) · Proceedings of the 16th ACM Conference on Recommender Systems · doi:10.1145/3523227.3546767 · Cited by 556
Adapting Large Language Models by Integrating Collaborative Semantics for Recommendation
Zheng, B., Hou, Y., Lu, H., Chen, Y., Zhao, W. X., Chen, M., & Wen, J.-R. (2024) · 2024 IEEE 40th International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00118 · Cited by 143
Xu, T., & Li, B. (2025) · Electronics · doi:10.3390/electronics14010154 · Cited by 11
Metadata
- Authors
- Ma, C., Kang, P., & Liu, X.
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 825-833
- OpenAlex
- W2951645301
- Cited by
- 322
- References
- 42
- FWCI
- 26.73
- Type
- conference paper
- Language
- en
- Open access
- green
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