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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AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
Bin Liu,Chenxu Zhu,Guilin Li,Weinan Zhang,Jincai Lai,Ruiming Tang,Xiuqiang He,Zhenguo Li,Yong Yu +8 more
TL;DR: This work proposes a two-stage algorithm called Automatic Feature Interaction Selection (AutoFIS), which can automatically identify important feature interactions for factorization models with computational cost just equivalent to training the target model to convergence.
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Attention-based context-aware sequential recommendation model
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Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling.
TL;DR: The proposed DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
Proceedings ArticleDOI
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
TL;DR: HierTCN as discussed by the authors is a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequential multi-session interactions with items, which is designed for web-scale systems with billions of items and hundreds of millions of users.
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Interpretable Click-Through Rate Prediction through Hierarchical Attention
TL;DR: InterHAt is proposed that employs a Transformer with multi-head self-attention for feature learning that captures high-order feature interactions by an efficient attentional aggregation strategy with low computational complexity.
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