DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

@inproceedings{Guo2017DeepFMAF,
  title={DeepFM: A Factorization-Machine based Neural Network for CTR Prediction},
  author={Huifeng Guo and Ruiming Tang and Yunming Ye and Zhenguo Li and Xiuqiang He},
  booktitle={IJCAI},
  year={2017}
}
Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. [] Key Method 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…

DeepFaFM :A Field-array Factorization Machine based Neural Network for CTR Prediction

  • Xiaowan ZhouYuliang Shi
  • Computer Science
    2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
  • 2020
TLDR
A new model called DeepFaFM is proposed, which provides a scalable coding matrix with adjustable factors, which can dynamically balance the operation pressure according to the actual production situation, improving the flexibility of the model.

An Attention-based Deep Network for CTR Prediction

TLDR
This paper proposes a deep CTR prediction model based on attention mechanism and GRU model, which can make use of the users' historical behaviors and can improve the prediction performance by extracting the implied interest features from user historical behaviors.

A Sparse Deep Factorization Machine for Efficient CTR prediction.

TLDR
This work proposes to prune the redundant parameters for the first time to accelerate the inference and reduce the run-time memory usage in embedding-based neural networks for online advertising.

Joint Deep Model with Multi-Level Attention and Hybrid-Prediction for Recommendation †

TLDR
A multi-level attention mechanism to learn the usefulness of reviews and the significance of words by Deep Neural Networks and a hybrid prediction structure that integrates Factorization Machine and DNN to model low-order user–item interactions and capture the high-order interactions as in DNN are designed.

Wide & ResNet: An Improved Network for CTR Prediction

TLDR
This paper proposes an improved network structure for CTR Prediction, Wide & ResNet (WRN), which still keeps the linear model Logistic Regression but introduces the idea of residual network in DNN component, which significantly outperforms the Wide & Deep model.

A Hybrid Neural Network Model with Non-linear Factorization Machines for Collaborative Recommendation

TLDR
A novel model Non-Linear Factorization Machine (NLFM) for modelling user-item interaction function and a hybrid deep model named AE-NLFM for collaborative recommendation that significantly outperforms the state-of-the-art methods.

Deep Field-Aware Interaction Machine for Click-Through Rate Prediction

TLDR
This work proposes a novel neural CTR model named DeepFIM by introducing Field-aware Interaction Machine (FIM), which provides a layered structure form to describe intrafield and interfield feature interaction, to solve the short-expression problem caused by the short feature-length in the field.

Feature Interaction based Neural Network for Click-Through Rate Prediction

TLDR
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.

FAT-DeepFFM: Field Attentive Deep Field-aware Factorization Machine

TLDR
A new neural CTR model named Field Attentive Deep Field-aware Factorization Machine (FAT-DeepFFM) is proposed by us as an enhanced version of Squeeze-Excitation network (SENet) to highlight the feature importance.
...

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