AutoRec: Autoencoders Meet Collaborative Filtering

被引：749
|
作者：
Sedhain, Suvash [1 ,2 ]
Menon, Aditya Krishna [1 ,2 ]
Sanner, Scott [1 ,2 ]
Xie, Lexing [1 ,2 ]
机构：
[1] NICTA, Sydney, NSW, Australia
[2] Australian Natl Univ, Canberra, ACT 0200, Australia
来源：
WWW'15 COMPANION: PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB | 2015年
关键词：
Recommender Systems; Collaborative Filtering; Autoencoders;
D O I：
10.1145/2740908.2742726
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBMCF and LLORMA) on the Movielens and Netflix datasets.
引用
收藏
页码：111 / 112
页数：2
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