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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Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-Based Feature Extractor
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AutoLoss: Automated Loss Function Search in Recommendations
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Fajie Yuan,Guoxiao Zhang,Alexandros Karatzoglou,Xiangnan He,Joemon M. Jose,Beibei Kong,Yudong Li +6 more
TL;DR: This paper presents Conure the first continual, or lifelong, user representation learner, and proposes iteratively removing less important weights of old tasks in a deep user representation model, motivated by the fact that neural network models are usually over-parameterized.
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Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach
TL;DR: A collaborative filtering method which is an ensemble of matrix factorization method and a neural network to solve the problem of Identification of best parent combinations for crossing and suggested that the proposed model significantly outperformed other models such as deep factorization machines (DeepFM) and neural networks.
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DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving.
TL;DR: DeepLight is presented, a framework to accelerate the CTR predictions in three aspects: accelerate the model inference via explicitly searching informative feature interactions in the shallow component; prune redundant parameters at the inter-layer level in the DNN component; and prune the dense embedding vectors to make them sparse in the embedding matrix.
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