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
Hierarchical Task-aware Multi-Head Attention Network
TL;DR: A neural multi-task learning model coined Hierarchical Task-aware Multi-headed Attention Network (HTMN), which explicitly distinguishes task-specific features from task-shared features to reduce the impact caused by weak correlation between tasks.
Proceedings ArticleDOI
A Large Scale Content Ranking Platform as Applied to E-commerce Store Fronts
TL;DR: The various considerations and challenges in building a ranking platform for promotional content at Flipkart are described and the architecture and solutions used in practice are described to provide a comprehensive overview of such a system.
Proceedings ArticleDOI
A Large Scale Content Ranking Platform as Applied to E-commerce Store Fronts
TL;DR: In this article , the authors describe the various considerations and challenges in building a ranking platform for these content at Flipkart, and delve into the solutions used in practice, in modelling, training, scaling, data correctness, freshness and metrics to provide a comprehensive overview of such a system.
Journal ArticleDOI
Will you go where you search? A deep learning framework for estimating user search-and-go behavior
TL;DR: In this article , a Deep Spatial-Temporal Interaction Network (DeepSTIN) is designed to automatically learn the sophisticated spatiotemporal interactions between mobility data and search query data.
Journal ArticleDOI
Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to Rank
TL;DR: In this paper, the authors propose to learn to rank from logged user feedback, such as clicks or purchases, which is a central component of many real-world information systems, including online advertising.
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.