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Dense Deep Crossing Network for Recommender System
Lin Wanying 1,Wang Yulong 2 *
1.State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing 100876;State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing 100876
2.State Key Laboratory of Networking and Switching, Beijing University of Posts and Telecommunications, Beijing 100876
*Correspondence author
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Funding: none
Opened online:21 December 2018
Accepted by: none
Citation: Lin Wanying,Wang Yulong.Dense Deep Crossing Network for Recommender System[OL]. [21 December 2018] http://en.paper.edu.cn/en_releasepaper/content/4746727
 
 
Manual feature engineering has been the key to the success of many predictive tasks of web applications. However, with the exponential increase in the variety and the volume of features, manual feature engineering comes with high cost. Factorization Machines are able to automatically learn the second-order feature interactions. However, FM models capture the non-linear structure of real-world data in an insufficient way. And recent work has shown that DNNs are able to learn higher-order interactions based on existing ones. In this paper, we propose a Deep Dense Crossing Network (DDCN) for recommender system. DDCN keeps the benefits of a DNN model and propose a novel dense crossing structure which connects each layer to every other layer in a feed-forward fashion. DDCN has several advantages: strengthen feature propagation, encourage feature reuse and implicitly learn feature crossing in an efficiently way. We evaluate the model on two datasets of hotel recommendation and clothes recommendation and our experimental results have demonstrated its superiority over the state-of-art algorithms on the recommendation dataset, in terms of model accuracy.
Keywords:Deep Learning; Neural Networks; Recommender System; DenseNet; Crossing Features
 
 
 

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