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- Title
Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems.
- Authors
Lara-Cabrera, Raúl; González-Prieto, Ángel; Ortega, Fernando
- Abstract
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.
- Subjects
MATRIX decomposition; RECOMMENDER systems; DEEP learning; FORECASTING; INFORMATION resources
- Publication
Applied Sciences (2076-3417), 2020, Vol 10, Issue 14, p4926
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app10144926