Locally Connected Deep Learning Framework for Industrial-scale Recommender Systems

Cen Chen, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li, Minghui Qiu

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 the recently proposed Wide&Deep model. Experiments on industrial-scale datasets show that our methods could achieve good results with much shorter runtime.