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
Citations
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Joint Deep Network With Auxiliary Semantic Learning for Popular Recommendation
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Learning to Retrieve User Behaviors for Click-Through Rate Estimation
Jiarui Qin,Weinan Zhang,Rong Su,Zhirong Liu,Weiwen Liu,Guangpeng Zhao,Hao Li,Ruiming Tang,Xiuqiang He,Yong Yu +9 more
TL;DR: Zhang et al. as mentioned in this paper proposed the User Behavior Retrieval (UBR) framework which aims at learning to retrieve the most informative user behaviors according to each CTR estimation request.
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Feature Interaction based Neural Network for Click-Through Rate Prediction.
TL;DR: A Feature Interaction based Neural Network (FINN) which is able to model feature interaction via a 3-dimention relation tensor and provides representations for the feature interactions on the bottom layer and the non-linearity of neural network in modelling higher-order feature interactions.
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Modeling personalized representation for within-basket recommendation based on deep learning
Xuguang Bao,Haolin Tang +1 more
TL;DR: In this paper , a deep learning-based model named DBFM (Deep Basket-Sensitive Factorization Machine) is proposed to address the task of within-basket recommendation, which predicts the related item to be added to the basket from the item corpus.
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