AutoRec: Autoencoders Meet Collaborative Filtering

被引：839

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

页码：111 / 112

页数：2