Dieses Papier schlägt AutoRec vor, ein neues Autoencoder-Rahmenwerk für kollaboratives Filtern (CF). Empirisch übertrifft das kompakte und effizient trainierbare Modell von AutoRec die neuesten CF-Techniken (verzerrte Matrixfaktorierung, RBM-CF und LLORMA) in den Movielens- und Netflix-Datensätzen.

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Cite This Study

Sedhain et al. (Mon,) untersuchten diese Frage.

synapsesocial.com/papers/69d8fde77e3358c846d17d3e https://doi.org/https://doi.org/10.1145/2740908.2742726

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Item-based collaborative filtering recommendation algorithms2001 · 9,014 citations
2Local Low-Rank Matrix Approximation2013 · 160 citations
3A direct adaptive method for faster backpropagation learning: the RPROP algorithm2002 · 3,925 citations
4Restricted Boltzmann machines for collaborative filtering2007 · 1,881 citations
5Matrix Factorization Techniques for Recommender Systems2009 · 11,663 citations

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Item-based collaborative filtering recommendation algorithms2001 · 9,014 citations
2Local Low-Rank Matrix Approximation2013 · 160 citations
3A direct adaptive method for faster backpropagation learning: the RPROP algorithm2002 · 3,925 citations
4Restricted Boltzmann machines for collaborative filtering2007 · 1,881 citations
5Matrix Factorization Techniques for Recommender Systems2009 · 11,663 citations

AutoRec | Synapse