Using Dynamic Multi-Task Non-Negative Matrix Factorization to Detect the Evolution of User Preferences in Collaborative Filtering

Predicting what items will be selected by a target user in the future is an important function for recommendation systems. Matrix factorization techniques have been shown to achieve good performance on temporal rating-type data, but little is known about temporal item selection data. In this paper,...

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Detalles Bibliográficos
Autores principales: Ju, Bin, Qian, Yuntao, Ye, Minchao, Ni, Rong, Zhu, Chenxi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535854/
https://www.ncbi.nlm.nih.gov/pubmed/26270539
http://dx.doi.org/10.1371/journal.pone.0135090

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4535854/
https://www.ncbi.nlm.nih.gov/pubmed/26270539
http://dx.doi.org/10.1371/journal.pone.0135090

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