Abstract
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.
Citation impact
1,176
total citations
- FWCI
- 106.33
- Percentile
- 100%
- References
- 6
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Authors
4Topics & keywords
Topics
Keywords
- MovieLens
- Computer science
- Matrix decomposition
- Autoencoder
- Collaborative filtering
- Artificial intelligence
- Factorization
- Recommender system
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