@inproceedings{2822725ebb18472889d3a00299d8f7aa,
title = "Locally connected deep learning framework for industrial-scale recommender systems",
abstract = "In this work, we propose a locally connected deep learning framework for recommender systems, which reduces the complexity of deep neural network (DNN) by two to three orders of magnitude. We further extend the framework using the idea of recently proposed Wide\&Deep model. Experiments on industrial-scale datasets show that our methods could achieve good results with much shorter runtime.",
keywords = "DNN, Locally-Connected DNN, Wide\&Deep",
author = "Cen Chen and Peilin Zhao and Longfei Li and Jun Zhou and Xiaolong Li and Minghui Qiu",
note = "Publisher Copyright: {\textcopyright} 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.; 26th International World Wide Web Conference, WWW 2017 Companion ; Conference date: 03-04-2017 Through 07-04-2017",
year = "2017",
doi = "10.1145/3041021.3054227",
language = "英语",
series = "26th International World Wide Web Conference 2017, WWW 2017 Companion",
publisher = "International World Wide Web Conferences Steering Committee",
pages = "769--770",
booktitle = "26th International World Wide Web Conference 2017, WWW 2017 Companion",
}