Paper Info
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 the recently proposed Wide&Deep model. Experiments on industrial-scale datasets show that our methods could achieve good results with much shorter runtime.
Year
DOI
Venue
2017
10.1145/3041021.3054227
WWW (Companion Volume)
Field
DocType
Citations 
Recommender system,Computer science,Artificial intelligence,Deep learning,Artificial neural network,Machine learning
Conference
6
PageRank 
References 
Authors
0.47
4
6
Name
Order
Citations
PageRank
Chen Cen116225.61
Peilin Zhao2136580.09
Longfei Li34411.22
Jun Zhou4101.90
Xiaolong Li536236.92
Minghui Qiu660.81