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DeepFM: A Factorization-Machine based Neural Network …

deepfm : A Factorization-Machine based Neural Network for CTR PredictionHuifeng Guo 1, Ruiming Tang2, Yunming Yey1, Zhenguo Li2, Xiuqiang He21 Shenzhen Graduate School, Harbin Institute of Technology, China2 Noah s Ark Research Lab, Huawei, , sophisticated feature interactions behinduser behaviors is critical in maximizing CTR forrecommender systems. Despite great progress, ex-isting methods seem to have a strong bias towardslow- or high-order interactions, or require exper-tise feature engineering. In this paper, we showthat it is possible to derive an end-to-end learn-ing model that emphasizes both low- and high-order feature interactions. The proposed model, deepfm , combines the power of factorization ma-chines for recommendation and deep learning forfeature learning in a new Neural Network architec-ture.

^y= sigmoid(y FM + y DNN); (1) where^y 2 (0; 1) is the predicted CTR,y FM is the output of FM component, andy DNN is the output of deep component. FM Component Figure 2: The architecture of FM. The FM component is a factorization machine, which is proposed in[Rendle, 2010] to learn feature interactions for recommendation. Besides a linear ...

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