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

  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},
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
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

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.

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 †

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

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

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

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

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

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.



Joint Deep Modeling of Users and Items Using Reviews for Recommendation

A deep model to learn item properties and user behaviors jointly from review text, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers.

Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction

This paper proposes two novel models using deep neural networks (DNNs) to automatically learn effective patterns from categorical feature interactions and make predictions of users' ad clicks and demonstrates that their methods work better than major state-of-the-art models.

Product-Based Neural Networks for User Response Prediction

  • Yanru QuHan Cai Jun Wang
  • Computer Science
    2016 IEEE 16th International Conference on Data Mining (ICDM)
  • 2016
A Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfield categories, and further fully connected layers to explore high-order feature interactions.

Field-aware Factorization Machines for CTR Prediction

This paper establishes FFMs as an effective method for classifying large sparse data including those from CTR prediction, and proposes efficient implementations for training FFMs and comprehensively analyze FFMs.

Collaborative Denoising Auto-Encoders for Top-N Recommender Systems

It is demonstrated that the proposed model is a generalization of several well-known collaborative filtering models but with more flexible components, and that CDAE consistently outperforms state-of-the-art top-N recommendation methods on a variety of common evaluation metrics.

Collaborative Deep Learning for Recommender Systems

A hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix is proposed, which can significantly advance the state of the art.

Deep content-based music recommendation

This paper proposes to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data, and shows that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.

A Convolutional Click Prediction Model

A novel model, Convolutional Click Prediction Model (CCPM), based on convolution neural network, that can extract local-global key features from an input instance with varied elements, which can be implemented for not only single ad impression but also sequential ad impression.

Deep CTR Prediction in Display Advertising

A novel deep neural network (DNN) based model is introduced that directly predicts the CTR of an image ad based on raw image pixels and other basic features in one step.

Pairwise interaction tensor factorization for personalized tag recommendation

The factorization model PITF (Pairwise Interaction Tensor Factorization) is presented which is a special case of the TD model with linear runtime both for learning and prediction and shows that this model outperforms TD largely in runtime and even can achieve better prediction quality.