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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Factorization Machine Based Service Recommendation on Heterogeneous Information Networks
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Learning Effective Representations for Person-Job Fit by Feature Fusion
TL;DR: Zhang et al. as mentioned in this paper proposed to learn comprehensive and effective representations of the candidates and job posts via feature fusion by extracting semantic entities from the whole resume (and job post) and then learn features for them.
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Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training
TL;DR: Zhang et al. as mentioned in this paper proposed adversarial poisoning training (APT), which simulates the poisoning process by injecting fake users (ERM users) who are dedicated to minimizing empirical risk to build a robust system.
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Click-through rate prediction in online advertising: A literature review
Yanyu Yang,Panyu Zhai +1 more
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DKEN: Deep knowledge-enhanced network for recommender systems
TL;DR: A principled deep knowledge-enhanced network (DKEN) framework based on deep learning and KGE to model the semantics of entities and relations encoded in knowledge graph (KG) and achieves remarkably better performance than several state-of-the-art baselines.
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