DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo,Ruiming Tang,Yunming Ye,Zhenguo Li,Xiuqiang He +4 more
- pp 1725-1731
TLDR
This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.Abstract:
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering. In this paper, we show that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions. The proposed model, DeepFM, combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture. Compared to the latest Wide & Deep model from Google, DeepFM has a shared input to its "wide" and "deep" parts, with no need of feature engineering besides raw features. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of DeepFM over the existing models for CTR prediction, on both benchmark data and commercial data.read more
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One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
Xiang-Rong Sheng,Liqin Zhao,Guorui Zhou,Xinyao Ding,Binding Dai,Qiang Luo,Siran Yang,Jingshan Lv,Chi Zhang,Hongbo Deng,Xiaoqiang Zhu +10 more
TL;DR: In this article, a Star Topology Adaptive Recommender (STAR) is proposed to learn an effective and efficient CTR model to handle multiple domains simultaneously, which consists of the shared centered parameters and domain-specific parameters.
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