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
More filters
Proceedings ArticleDOI
RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate Prediction
TL;DR: In this article, a reinforcement learning based noise filtering approach, dubbed RLNF, was proposed to select effective negative samples to enhance the CTR prediction model, and meanwhile the effectiveness of the noise filter can be enhanced through reinforcement learning using the performance of CTR prediction models as reward.
Journal ArticleDOI
Predicting Fine-Grained Air Quality Based on Deep Neural Networks
TL;DR: A real-time system on the cloud, providing fine-grained air quality forecasts for 300 Chinese cities every hour, mainly consisting of three components: data crawler, task scheduler, and prediction model, which are implemented with a multi-task architecture to improve the system's efficiency and stability.
Journal ArticleDOI
XGBDeepFM for CTR Predictions in Mobile Advertising Benefits from Ad Context
TL;DR: A method was developed to obtain accurate CTR prediction by incorporating contextual features and feature interactions in “wide and deep” type of model and many experiments show that the XGBDeepFM model has better value of area under curve and improves the effectiveness and efficiency of CTR prediction for mobile advertising.
Proceedings ArticleDOI
Bars
TL;DR: In this article , the authors present an initiative project aimed for open benchmarking for recommender systems, which integrates all the details about datasets, source code, hyper-parameter settings, running logs, and evaluation results.
Journal ArticleDOI
Where to Go Next for Recommender Systems? ID- vs. Modality-based recommender models revisited
TL;DR: In this paper , a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal Article
Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Proceedings ArticleDOI
Deep Neural Networks for YouTube Recommendations
TL;DR: This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.
Proceedings ArticleDOI
Factorization Machines
TL;DR: Factorization Machines (FM) are introduced which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models and can mimic these models just by specifying the input data (i.e. the feature vectors).
Proceedings ArticleDOI
Restricted Boltzmann machines for collaborative filtering
TL;DR: This paper shows how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies, and demonstrates that RBM's can be successfully applied to the Netflix data set.