The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Fast context-aware recommendations with factorization machines
Abstract
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of context-aware methods.
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Citations
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
Cited by
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He, X., Zhang, H., Kan, M.-Y., & Chua, T.-S. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911489 · Cited by 988
Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., & Chua, T.-S. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/435 · Cited by 912
Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation
Lu, Y., Fang, Y., & Shi, C. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403207 · Cited by 259
Wang, H., Xu, T., Liu, Q., Lian, D., Chen, E., Du, D., Wu, H., & Su, W. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330931 · Cited by 141
PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion
Zhang, S., Yin, H., Chen, T., Huang, Z., Nguyen, Q. V. H., & Cui, L. (2022) · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3488560.3498386 · Cited by 98
Metadata
- Authors
- Rendle, S., Gantner, Z., Freudenthaler, C., & Schmidt-Thieme, L.
- Venue
- Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
- Publisher
- ACM
- Published
- 2011-07-24
- Pages
- 635-644
- OpenAlex
- W2002834872
- Cited by
- 574
- References
- 1
- FWCI
- 13.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rendle, S., Gantner, Z., Freudenthaler, C., & Schmidt-Thieme, L. (2011). Fast context-aware recommendations with factorization machines. Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. https://doi.org/10.1145/2009916.2010002
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