A Dual Input-Aware Factorization Machine for CTR Prediction
Abstract
Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction. However, standard FMs only produce a single fixed representation for each feature across different input instances, which may limit the CTR model’s expressive and predictive power. Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model. Comprehensive experiments on two real-world CTR prediction datasets show that the DIFM model can outperform several state-of-the-art models consistently.
Cite
Text
Lu et al. "A Dual Input-Aware Factorization Machine for CTR Prediction." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/434Markdown
[Lu et al. "A Dual Input-Aware Factorization Machine for CTR Prediction." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/lu2020ijcai-dual/) doi:10.24963/IJCAI.2020/434BibTeX
@inproceedings{lu2020ijcai-dual,
title = {{A Dual Input-Aware Factorization Machine for CTR Prediction}},
author = {Lu, Wantong and Yu, Yantao and Chang, Yongzhe and Wang, Zhen and Li, Chenhui and Yuan, Bo},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {3139-3145},
doi = {10.24963/IJCAI.2020/434},
url = {https://mlanthology.org/ijcai/2020/lu2020ijcai-dual/}
}