CoRec: a co-training approach for recommender systems

Da Costa, Arthur F., Manzato, Marcelo G., and Campello, Ricardo J.G.B. (2018) CoRec: a co-training approach for recommender systems. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing. pp. 696-703. From: SAC 18: 33rd Annual ACM Symposium on Applied Computing, 9-13 April 2018, Pau, France.

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In Recommender Systems, a large amount of labeled data must be available beforehand to obtain good predictions. However, labeled data are often limited and expensive to obtain, since labeling typically requires human expertise, time, and labor. This paper proposes a framework, named CoRec, which is based on a co-training approach that drives two recommenders to agree with each other's predictions to generate their own. We used three publicly available datasets from movies, jokes and books domains, as well as two well-known recommender algorithms, to demonstrate the efficiency of the approach under different configurations. The experiments show that better accuracy can be obtained when recommender algorithms are simultaneously co-trained from multiple views to make predictions.

Item ID: 58499
Item Type: Conference Item (Research - E1)
ISBN: 978-1-4503-5191-1
Keywords: Co-training, Recommender systems,Semi-supervised learning
Projects and Grants: FAPESP 2016/20280-6
Date Deposited: 04 Jun 2019 02:10
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461106 Semi- and unsupervised learning @ 100%
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