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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Proceedings ArticleDOI
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
TL;DR: This work proposes an automated interaction architecture discovering framework for CTR prediction named AutoCTR, which performs evolutionary architecture exploration with learning-to-rank guidance at the architecture level and achieves acceleration using low-fidelity model.
Proceedings ArticleDOI
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
TL;DR: This work develops a distance-based recommendation model with several novel aspects, including each user and item are parameterized by Gaussian distributions to capture the learning uncertainties and an adaptive margin generation scheme is proposed to generate the margins regarding different training triplets.
Proceedings ArticleDOI
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Siyuan Guo,Lixin Zou,Yiding Liu,Wenwen Ye,Suqi Cheng,Shuaiqiang Wang,Hechang Chen,Dawei Yin,Yi Chang +8 more
TL;DR: Zhang et al. as mentioned in this paper proposed a doubly robust estimator to estimate the post-click conversion rate (CVR), which can reduce the bias and variance of the DR estimator while retaining double robustness.
Proceedings ArticleDOI
AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction
TL;DR: An end-to-end model, AutoGroup, is proposed, which casts the selection of feature interactions as a structural optimization problem and performs both dimensionality reduction and feature selection which are not seen in previous models.
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Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review
Carmela Comito,Clara Pizzuti +1 more
TL;DR: A comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19 can be found in this paper , where the authors provide a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved.
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