The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
List-wise learning to rank with matrix factorization for collaborative filtering
Abstract
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is obtained by minimizing a loss function that represents the uncertainty between training lists and output lists produced by a MF ranking model. ListRank-MF enjoys the advantage of low complexity and is analytically shown to be linear with the number of observed ratings for a given user-item matrix. We also experimentally demonstrate the effectiveness of ListRank-MF by comparing its performance with that of item-based collaborative recommendation and a related state-of-the-art collaborative ranking approach (CoFiRank).
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Cites
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273513 · Cited by 1,960
Cited by
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
Neural Rating Regression with Abstractive Tips Generation for Recommendation
Li, P., Wang, Z., Ren, Z., Bing, L., & Lam, W. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080822 · Cited by 313
Optimizing top-n collaborative filtering via dynamic negative item sampling
Zhang, W., Chen, T., Wang, J., & Yu, Y. (2013) · Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2484028.2484126 · Cited by 256
Metadata
- Authors
- Shi, Y., Larson, M., & Hanjalic, A.
- Venue
- Proceedings of the fourth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2010-09-26
- Pages
- 269-272
- OpenAlex
- W2071111773
- Cited by
- 246
- References
- 2
- FWCI
- 4.50
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shi, Y., Larson, M., & Hanjalic, A. (2010). List-wise learning to rank with matrix factorization for collaborative filtering. Proceedings of the fourth ACM conference on Recommender systems. https://doi.org/10.1145/1864708.1864764
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
