DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
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Summary
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.
- Type
- preprint
- Published
- 2017-03-13
- Cited by
- 3,183
- References
- 32
- Access
- Open access
- OpenAlex
- https://openalex.org/W2604662567
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:970388
Keywords
Feature engineering, Computer science, Benchmark (surveying), Feature (linguistics), Artificial intelligence
References
- AutoRec: Autoencoders Meet Collaborative Filtering
- A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data
- A Convolutional Click Prediction Model
- Improving Content-based and Hybrid Music Recommendation using Deep Learning
- Ad click prediction: a view from the trenches
- Practical Lessons from Predicting Clicks on Ads at Facebook
- Pairwise interaction tensor factorization for personalized tag recommendation
- Predicting clicks: estimating the click-through rate for new ads
- Dropout: a simple way to prevent neural networks from overfitting
- Restricted Boltzmann machines for collaborative filtering
- Deep content-based music recommendation
- Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
- Collaborative Deep Learning for Recommender Systems
- Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine
- Deep Residual Learning for Image Recognition
- Collaborative Denoising Auto-Encoders for Top-N Recommender Systems
- Factorization Machines
- Audio Chord Recognition with Recurrent Neural Networks
- Wide & Deep Learning for Recommender Systems
- Field-aware Factorization Machines for CTR Prediction
Cited by
- Deep Interest Network for Click-Through Rate Prediction
- Deep Learning Based Recommender System
- Deep Learning for Recommender Systems
- Practical Lessons for Job Recommendations in the Cold-Start Scenario
- Deep Learning-Based Recommendation: Current Issues and Challenges
- CPU versus GPU: which can perform matrix computation faster—performance comparison for basic linear algebra subprograms
- Image Matters: Visually Modeling User Behaviors Using Advanced Model Server
- Structured Deep Factorization Machine: Towards General-Purpose Architectures
- Attribute-aware Collaborative Filtering: Survey and Classification
- Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
- Learning from Multi-View Multi-Way Data via Structural Factorization Machines
- Federated Meta-Learning for Recommendation
- xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
- Broad Learning for Healthcare
- DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction
- Deep Factorization Machines for Knowledge Tracing
- Multi-Level Deep Cascade Trees for Conversion Rate Prediction
- TRSDL: Tag-Aware Recommender System Based on Deep Learning–Intelligent Computing Systems
- Deep hybrid collaborative filtering for Web service recommendation
- Self-Attentive Neural Collaborative Filtering
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