research-article

CoRec: a co-training approach for recommender systems

Authors:

Arthur F. da Costa,

Marcelo G. Manzato,

Ricardo J. G. B. CampelloAuthors Info & Claims

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Pages 696 - 703

https://doi.org/10.1145/3167132.3167209

Published: 09 April 2018 Publication History

Abstract

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.

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Cited By

Coscrato VBridge D(2023)Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender SystemsACM Transactions on Recommender Systems10.1145/35840211:2(1-34)Online publication date: 24-Apr-2023
https://doi.org/10.1145/3584021
Singh AKumar ASharma AKatarya R(2022)A Systematic Survey of Tourism Recommender System Techniques and ChallengesJournal of ISMAC10.36548/jismac.2021.4.0063:4(350-366)Online publication date: 2-May-2022
https://doi.org/10.36548/jismac.2021.4.006
Xia XYin HYu JShao YCui LDemartini GZuccon GCulpepper JHuang ZTong H(2021)Self-Supervised Graph Co-Training for Session-based RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482388(2180-2190)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482388
Show More Cited By

Index Terms

CoRec: a co-training approach for recommender systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

April 2018

2327 pages

ISBN:9781450351911

DOI:10.1145/3167132

Conference Chairs:
Hisham M. Haddad
Kennesaw State University
,
Roger L. Wainwright
University of Tulsa
,
Richard Chbeir
University of Pau & Pays Adour, France

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2018

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Funding Sources

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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SAC 2018

Sponsor:

SIGAPP

SAC 2018: Symposium on Applied Computing

April 9 - 13, 2018

Pau, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Citations

Cited By

Coscrato VBridge D(2023)Estimating and Evaluating the Uncertainty of Rating Predictions and Top-n Recommendations in Recommender SystemsACM Transactions on Recommender Systems10.1145/35840211:2(1-34)Online publication date: 24-Apr-2023
https://doi.org/10.1145/3584021
Singh AKumar ASharma AKatarya R(2022)A Systematic Survey of Tourism Recommender System Techniques and ChallengesJournal of ISMAC10.36548/jismac.2021.4.0063:4(350-366)Online publication date: 2-May-2022
https://doi.org/10.36548/jismac.2021.4.006
Xia XYin HYu JShao YCui LDemartini GZuccon GCulpepper JHuang ZTong H(2021)Self-Supervised Graph Co-Training for Session-based RecommendationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482388(2180-2190)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482388
Kamta SVerma V(2020)Accuracy Analysis of Similarity Measures in Surprise FrameworkEvolutionary Computing and Mobile Sustainable Networks10.1007/978-981-15-5258-8_80(861-873)Online publication date: 1-Aug-2020
https://doi.org/10.1007/978-981-15-5258-8_80
Tan WHe YZhu B(2020)Improvement of Co-training Based Recommender System with Machine LearningArtificial Intelligence and Security10.1007/978-3-030-57881-7_44(499-509)Online publication date: 1-Sep-2020
https://doi.org/10.1007/978-3-030-57881-7_44
da Costa AManzato MCampello R(2019)Boosting collaborative filtering with an ensemble of co-trained recommendersExpert Systems with Applications10.1016/j.eswa.2018.08.020115(427-441)Online publication date: Jan-2019
https://doi.org/10.1016/j.eswa.2018.08.020
Ravi LSubramaniyaswamy VVijayakumar VChen SKarmel ADevarajan M(2019)Hybrid Location-based Recommender System for Mobility and Travel PlanningMobile Networks and Applications10.1007/s11036-019-01260-424:4(1226-1239)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1007/s11036-019-01260-4

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