A Speed up Method for Collaborative Filtering with Autoencoders

被引：0

作者：

Tang, Wen-Zhe ^{[1
]}

Wang, Yi-Lei ^{[1
]}

Wu, Ying-Jie ^{[1
]}

Wang, Xiao-Dong ^{[1
]}

机构：

[1] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou, Fujian, Peoples R China

来源：

FUZZY SYSTEMS AND DATA MINING II | 2016年 / 293卷

关键词：

Recommender System; Neural Network; Auto-encoder;

D O I：

10.3233/978-1-61499-722-1-321

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Collaborative Filtering(CF) is a widely used technique in Recommender System. With recent development in deep learning, Neural network based CF has gained great attention in recent years, especially auto-encoders. However, the main disadvantage of autoencoder based CF is the problem of the large sparse target. In this paper, we propose a training strategy to tackle this issue, We run experiments on two popular real world datasets MovieLens 1M and MovieLens 10M. Experiments show orders of magnitude speed up while Attaining similar accuracy compare to existing autoencoder based CF method.

引用

页码：321 / 326

页数：6

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