The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Abstract
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.
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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
Restricted Boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885
Explaining collaborative filtering recommendations
Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000) · Proceedings of the 2000 ACM conference on Computer supported cooperative work · doi:10.1145/358916.358995 · Cited by 1,731
Cited by
Neural Graph Collaborative Filtering
Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3331184.3331267 · Cited by 3,823
Rendle, S. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.127 · Cited by 3,189
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management · doi:10.1145/3357384.3357895 · Cited by 2,558
The Netflix Recommender System: Algorithms, Business Value, and Innovation
Gomez-Uribe, C. A., & Hunt, N. (2016) · ACM Transactions on Management Information Systems · doi:10.1145/2843948 · Cited by 1,418
Context-Aware Recommender Systems
Adomavicius, G., & Tuzhilin, A. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_7 · Cited by 1,102
Metadata
- Authors
- Koren, Y.
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 426-434
- OpenAlex
- W1994389483
- Cited by
- 3,852
- References
- 3
- FWCI
- 53.51
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1401890.1401944
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