CoRec: A co-training approach for recommender systems

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

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review


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.

TitelProceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC 2018
ForlagAssociation for Computing Machinery
Publikationsdato9. apr. 2018
ISBN (Elektronisk)9781450351911
StatusUdgivet - 9. apr. 2018
Udgivet eksterntJa
Begivenhed33rd Annual ACM Symposium on Applied Computing, SAC 2018 - Pau, Frankrig
Varighed: 9. apr. 201813. apr. 2018


Konference33rd Annual ACM Symposium on Applied Computing, SAC 2018
SponsorACM Special Interest Group on Applied Computing (SIGAPP)

Bibliografisk note

Funding Information:
We would like to acknowledge CAPES and FAPESP (2016/20280-6) for the financial support.

Publisher Copyright:
© 2018 ACM.


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