AutoRec: Autoencoders Meet Collaborative Filtering

Published on May 18, 2015 in WWW (The Web Conference)
· DOI :10.1145/2740908.2742726
Suvash Sedhain5
Estimated H-index: 5
(ANU: Australian National University),
Aditya Krishna Menon22
Estimated H-index: 22
(ANU: Australian National University)
+ 1 AuthorsLexing Xie32
Estimated H-index: 32
(ANU: Australian National University)
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, RBM-CF and LLORMA) on the Movielens and Netflix datasets.
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