Quantum mechanics

Materials science

MovieLens

Eigenvalues and eigenvectors

Computer science

Non-negative matrix factorization

Matrix (chemical analysis)

Physics

Collaborative filtering

Pattern recognition (psychology)

Autoencoder

Artificial intelligence

Composite material

Deep learning

Recommender system

Matrix decomposition

Machine learning

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.

AutoRec