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
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Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
Z. Hugh Fan,Dan Ou,Yulong Gu,Bairan Fu,Xiang (Robert) Li,Wentian Bao,Xinyu Dai,Xiaoyi Zeng,Tao Zhuang,Qingwen Liu +9 more
TL;DR: A novel neural ranking model RACP(Recurrent Attention over Contextualized Page sequence), which utilizes page-context aware attention to model the intra-page context and the cross-page interest convergence evolution as denoising the interest in the previous pages.
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Leveraging User Profiling in Click-through Rate Prediction Based on Zhihu Data
TL;DR: A Profile-CTR model is proposed, which leverages user profiles and historical behavior data to predict CTR of certain items on Zhihu, a popular social Q&A platform, and shows that combining the user profiles with the historical behavior records can significantly improve the performance of the CTR prediction in the recommendation system.
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IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System
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Deep Learning Based Approaches for Recommendation Systems
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An Embedded Model XG-FwFMs for Click-Through Rate
TL;DR: A embedded model named XG-FwFMs which use less parameters calculating and prevent the model from over-fitting is proposed which has better prediction accuracy, parameter sensitivity and effectiveness than traditional nonlinear models.
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