The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Open accessAbstract
Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github.com/hexiangnan/attentional_factorization_machine
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
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SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
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Self-citationFactorization Machines with libFM
Rendle, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168771 · Cited by 1,337
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Fast Matrix Factorization for Online Recommendation with Implicit Feedback
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Self-citationField-aware Factorization Machines for CTR Prediction
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Visual Translation Embedding Network for Visual Relation Detection
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Self-citationFast context-aware recommendations with factorization machines
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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
Self-citationEfficient Federated Learning With Spike Neural Networks for Traffic Sign Recognition
Xie, K., Zhang, Z., Li, B., Kang, J., Niyato, D., Xie, S., & Wu, Y. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3178808 · Cited by 128
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Metadata
- Authors
- Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., & Chua, T.-S.
- Venue
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- Published
- 2017-08
- Pages
- 3119-3125
- OpenAlex
- W2964052347
- Cited by
- 912
- References
- 19
- FWCI
- 54.22
- Type
- conference paper
- Language
- en
- Open access
- bronze
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- Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., & Chua, T.-S. (2017). Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2017/435
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