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Field-aware Factorization Machines for CTR Prediction
Abstract
Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2021068 · Cited by 346
Cited by
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Wang, R., Fu, B., Fu, G., & Wang, M. (2017) · Proceedings of the ADKDD'17 · doi:10.1145/3124749.3124754 · Cited by 1,161
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
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
Online Display Advertising Markets: A Literature Review and Future Directions
Choi, H., Mela, C. F., Balseiro, S. R., & Leary, A. (2020) · Information Systems Research · doi:10.1287/isre.2019.0902 · Cited by 181
Liu, B., Zhu, C., Li, G., Zhang, W., Lai, J., Tang, R., He, X., Li, Z., & Yu, Y. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403314 · Cited by 171
Metadata
- Authors
- Juan, Y., Zhuang, Y., Chin, W.-S., & Lin, C.-J.
- Venue
- Proceedings of the 10th ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2016-09-07
- Pages
- 43-50
- OpenAlex
- W2509235963
- Cited by
- 712
- References
- 1
- FWCI
- 43.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Juan, Y., Zhuang, Y., Chin, W.-S., & Lin, C.-J. (2016). Field-aware Factorization Machines for CTR Prediction. Proceedings of the 10th ACM Conference on Recommender Systems. https://doi.org/10.1145/2959100.2959134
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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.
