The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Open accessAbstract
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.
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Cites
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Wide & Deep Learning for Recommender Systems
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Cited by
Knowledge Graph Convolutional Networks for Recommender Systems
Wang, H., Zhao, M., Xie, X., Li, W., & Guo, M. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313417 · Cited by 1,021
Self-citationExplainable Reasoning over Knowledge Graphs for Recommendation
Wang, X., Wang, D., Xu, C., He, X., Cao, Y., & Chua, T.-S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015329 · Cited by 785
Knowledge Graphs: Opportunities and Challenges
Peng, C., Xia, F., Naseriparsa, M., & Osborne, F. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10465-9 · Cited by 717
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
Zhao, W. X., Mu, S., Hou, Y., Lin, Z., Chen, Y., Pan, X., Li, K., Lu, Y., Wang, H., Tian, C., et al. (2021) · Proceedings of the 30th ACM International Conference on Information & Knowledge Management · doi:10.1145/3459637.3482016 · Cited by 455
Self-citationWhen large language models meet personalization: perspectives of challenges and opportunities
Chen, J., Liu, Z., Huang, X., Wu, C., Liu, Q., Jiang, G., Pu, Y., Lei, Y., Chen, X., Wang, X., et al. (2024) · World Wide Web · doi:10.1007/s11280-024-01276-1 · Cited by 367
Metadata
- Authors
- Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M.
- Venue
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Published
- 2018-10-17
- Pages
- 417-426
- OpenAlex
- W2792839191
- Cited by
- 1,160
- References
- 33
- FWCI
- 54.21
- Type
- conference paper
- Language
- en
- Open access
- green
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