A NOVEL LATENT FACTOR MODEL FOR RECOMMENDER SYSTEM
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| Format: | Artículo científico |
| Language: | en |
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Universidade de São Paulo
2016
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| _version_ | 1876469682859409408 |
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| author | Bipul Kumar |
| author_facet | Bipul Kumar |
| contents | A NOVEL LATENT FACTOR MODEL FOR RECOMMENDER SYSTEM Bipul Kumar Ingeniería commerce services complexity recommender system Latent factor model Matrix factorization (MF) has evolved as one of the better practice to handle sparse data in field of recommender systems. Funk singular value decomposition (SVD) is a varian t of MF that exists as state - of - the - art method that enabled winning the Netflix prize competition. The method is widely used with modifications in present day research in field of recommender systems. With the potential of data points to grow at very high velocity, it is prudent to devise newer methods that can handle such data accurately as well as efficiently than Funk - SVD in the context of recommender system. In view of the growing data points, I propose a latent factor model that caters to both accuracy and efficiency by reducing the number of latent features of either users or items making it less complex than Funk - SVD, where latent features of both users and items are equal and often larger. A comprehensive empirical evaluation of accuracy on two publicly available, amazon and ml - 100 k datasets reveals the comparable accuracy and lesser complexity of proposed methods than Funk - SVD. 2016 artículo científico 1807-1775 https://www.redalyc.org/articulo.oa?id=203249448008 en http://www.redalyc.org/revista.oa?id=2032 JISTEM: Journal of Information Systems and Technology Management application/pdf Universidade de São Paulo JISTEM: Journal of Information Systems and Technology Management (Brasil) Num.3 Vol.13 |
| format | Artículo científico |
| id | redalyc_203249448008 |
| institution | Redalyc |
| language | en |
| publishDate | 2016 |
| publisher | Universidade de São Paulo |
| spellingShingle | A NOVEL LATENT FACTOR MODEL FOR RECOMMENDER SYSTEM Bipul Kumar Ingeniería commerce services complexity recommender system Latent factor model A NOVEL LATENT FACTOR MODEL FOR RECOMMENDER SYSTEM Bipul Kumar Ingeniería commerce services complexity recommender system Latent factor model Matrix factorization (MF) has evolved as one of the better practice to handle sparse data in field of recommender systems. Funk singular value decomposition (SVD) is a varian t of MF that exists as state - of - the - art method that enabled winning the Netflix prize competition. The method is widely used with modifications in present day research in field of recommender systems. With the potential of data points to grow at very high velocity, it is prudent to devise newer methods that can handle such data accurately as well as efficiently than Funk - SVD in the context of recommender system. In view of the growing data points, I propose a latent factor model that caters to both accuracy and efficiency by reducing the number of latent features of either users or items making it less complex than Funk - SVD, where latent features of both users and items are equal and often larger. A comprehensive empirical evaluation of accuracy on two publicly available, amazon and ml - 100 k datasets reveals the comparable accuracy and lesser complexity of proposed methods than Funk - SVD. 2016 artículo científico 1807-1775 https://www.redalyc.org/articulo.oa?id=203249448008 en http://www.redalyc.org/revista.oa?id=2032 JISTEM: Journal of Information Systems and Technology Management application/pdf Universidade de São Paulo JISTEM: Journal of Information Systems and Technology Management (Brasil) Num.3 Vol.13 |
| title | A NOVEL LATENT FACTOR MODEL FOR RECOMMENDER SYSTEM |
| topic | Ingeniería commerce services complexity recommender system Latent factor model |
| url | https://www.redalyc.org/articulo.oa?id=203249448008 |