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AutoRec: Autoencoders Meet Collaborative Filtering
AutoRec: Autoencoders Meet Collaborative Filtering
Aditya Krishna Menon Scott Sanner Suvash Sedhain Lexing Xie
Abstract
This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec’s compact and efficiently trainable model outperforms stateof-the-art CF techniques (biased matrix factorization, RBMCF and LLORMA) on the Movielens and Netflix datasets.