{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.
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| collaborative-filtering-on-movielens-10m | I-AutoRec | RMSE: 0.782 |
| collaborative-filtering-on-movielens-1m | I-AutoRec | RMSE: 0.831 |
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