Collaborative filtering via sparse Markov random fields
Data(s) |
10/11/2016
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Resumo |
Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov random fields (MRF). We address the open problem of structure learning and introduce a sparsity-inducing algorithm to automatically estimate the interaction structures between users and between items. Item-item and user-user correlation networks are obtained as a by-product. Large-scale experiments on movie recommendation and date matching datasets demonstrate the power of the proposed method. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Elsevier |
Relação |
http://dro.deakin.edu.au/eserv/DU:30085679/tran-collaborativefiltering-2016.pdf http://dro.deakin.edu.au/eserv/DU:30085679/tran-collaborativefiltering-preprint-2016.pdf http://www.dx.doi.org/10.1016/j.ins.2016.06.027 |
Direitos |
2016, Elsevier |
Palavras-Chave | #recommender systems #collaborative filtering #Markov random field #sparse graph learning #movie recommendation #dating recommendation |
Tipo |
Journal Article |