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Co-Training-Teaching: A Robust Semi-Supervised Framework for Review-Aware Rating Regression

Authors:

Jianbo YuanAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 2

Article No.: 41, Pages 1 - 16

https://doi.org/10.1145/3625391

Published: 13 November 2023 Publication History

Abstract

Review-aware Rating Regression (RaRR) suffers the severe challenge of extreme data sparsity as the multi-modality interactions of ratings accompanied by reviews are costly to obtain. Although some studies of semi-supervised rating regression are proposed to mitigate the impact of sparse data, they bear the risk of learning from noisy pseudo-labeled data. In this article, we propose a simple yet effective paradigm, called co-training-teaching (CoT²), for integrating the merits of both co-training and co-teaching toward robust semi-supervised RaRR. CoT² employs two predictors trained with different feature sets of textual reviews, each of which functions as both “labeler” and “validator.” Specifically, one predictor (labeler) first labels unlabeled data for its peer predictor (validator); after that, the validator samples reliable instances from the noisy pseudo-labeled data it received and sends them back to the labeler for updating. By exchanging and validating pseudo-labeled instances, the two predictors are reinforced by each other in an iterative learning process. The final prediction is made by averaging the outputs of both the refined predictors. Extensive experiments show that our CoT² considerably outperforms the state-of-the-art recommendation techniques in the RaRR task, especially when the training data is severely insufficient.

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Cited By

Chen MWang TXu CChen JChen EPan Z(2024)Gradient Prior Guidance and Image Adaptation Enhancement for Semi-Supervised SAR Ship Instance SegmentationIEEE Sensors Journal10.1109/JSEN.2024.346703024:21(36216-36229)Online publication date: 1-Nov-2024
https://doi.org/10.1109/JSEN.2024.3467030
Chen WCai ZLin PHuang YDu SGuo WWang S(2024)Multi-view semi-supervised classification via auto-weighted submarkov random walkExpert Systems with Applications10.1016/j.eswa.2024.124961256(124961)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124961
Zhong WZhang YWu S(2024)Environment-Independent Fusion for Robust Object Detection in Adverse EnvironmentsPattern Recognition10.1007/978-3-031-78110-0_28(429-444)Online publication date: 2-Dec-2024
https://doi.org/10.1007/978-3-031-78110-0_28

Index Terms

Co-Training-Teaching: A Robust Semi-Supervised Framework for Review-Aware Rating Regression
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 2

February 2024

401 pages

EISSN:1556-472X

DOI:10.1145/3613562

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 November 2023

Online AM: 26 September 2023

Accepted: 06 September 2023

Revised: 23 March 2023

Received: 09 March 2022

Published in TKDD Volume 18, Issue 2

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Fundamental Research Funds for the Central Universities
Open Research Fund from the Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen

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Cited By

Chen MWang TXu CChen JChen EPan Z(2024)Gradient Prior Guidance and Image Adaptation Enhancement for Semi-Supervised SAR Ship Instance SegmentationIEEE Sensors Journal10.1109/JSEN.2024.346703024:21(36216-36229)Online publication date: 1-Nov-2024
https://doi.org/10.1109/JSEN.2024.3467030
Chen WCai ZLin PHuang YDu SGuo WWang S(2024)Multi-view semi-supervised classification via auto-weighted submarkov random walkExpert Systems with Applications10.1016/j.eswa.2024.124961256(124961)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124961
Zhong WZhang YWu S(2024)Environment-Independent Fusion for Robust Object Detection in Adverse EnvironmentsPattern Recognition10.1007/978-3-031-78110-0_28(429-444)Online publication date: 2-Dec-2024
https://doi.org/10.1007/978-3-031-78110-0_28

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