期刊
APPLIED SCIENCES-BASEL
卷 10, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/app10144926
关键词
deep learning; recommender systems; collaborative filtering; matrix factorization
类别
资金
- Spanish Ministry of Science and Education and Competitivity (MINECO)
- European Regional Development Fund (FEDER) [TIN2017-85727-C4-3-P]
Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.
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