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• W2951001079 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." @default.
• W2951001079 created "2019-06-27" @default.
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• W2951001079 date "2017-03-13" @default.
• W2951001079 modified "2023-09-30" @default.
• W2951001079 title "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction" @default.
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• W2951001079 doi "https://doi.org/10.48550/arxiv.1703.04247" @default.
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