Papers › AutoRec: Autoencoders Meet Collaborative Filtering
AutoRec: Autoencoders Meet Collaborative Filtering
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | MovieLens 10M | I-AutoRec | RMSE | 0.782 | #10 of 17 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | I-AutoRec | RMSE | 0.831 | #5 of 31 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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