The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Contrastive Learning for Cold-Start Recommendation
Open accessAbstract
Recommending purely cold-start items is a long-standing and fundamental challenge in the recommender systems. Without any historical interaction on cold-start items, the collaborative filtering (CF) scheme fails to leverage collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information of items (e.g. content features) into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance on cold-start recommendation.
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Cites
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Self-supervised learning for recommender systems: A survey
Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3282907 · Cited by 413
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Wei, W., Ren, X., Tang, J., Wang, Q., Su, L., Cheng, S., Wang, J., Yin, D., & Huang, C. (2024) · Proceedings of the 17th ACM International Conference on Web Search and Data Mining · doi:10.1145/3616855.3635853 · Cited by 268
Metadata
- Authors
- Wei, Y., Wang, X., Li, Q., Nie, L., Li, Y., Li, X., & Chua, T.-S.
- Venue
- Proceedings of the 29th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2021-10-17
- Pages
- 5382-5390
- OpenAlex
- W3206310679
- Cited by
- 331
- References
- 32
- FWCI
- 47.02
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Wei, Y., Wang, X., Li, Q., Nie, L., Li, Y., Li, X., & Chua, T.-S. (2021). Contrastive Learning for Cold-Start Recommendation. Proceedings of the 29th ACM International Conference on Multimedia. https://doi.org/10.1145/3474085.3475665
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