Boosting collaborative filtering with an ensemble of co-trained recommenders

Author:

da Costa Arthur F.ORCID,Manzato Marcelo G.,Campello Ricardo J.G.B.

Funder

FAPESP

CNPq

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference38 articles.

1. Towards more confident recommendations: Improving recommender systems using filtering approach based on rating variance;Adomavicius,2007

2. Recommender systems: The textbook;Aggarwal,2016

3. Improving simple collaborative filtering models using ensemble methods;Bar,2013

4. Combining labeled and unlabeled data with co-training;Blum,1998

5. Reliability quality measures for recommender systems;Bobadilla;Information Sciences,2018

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