AAAI 2023technical0 citations
Semi-supervised Review-Aware Rating Regression (Student Abstract)
Abstract
Semi-supervised learning is a promising solution to mitigate data sparsity in review-aware rating regression (RaRR), but it bears the risk of learning with noisy pseudo-labelled data. In this paper, we propose a paradigm called co-training-teaching (CoT2), which integrates the merits of both co-training and co-teaching towards the robust semi-supervised RaRR. Concretely, CoT2 employs two predictors and each of them alternately plays the roles of "labeler" and "validator" to generate and validate pseudo-labelled instances. Extensive experiments show that CoT2 considerably outperforms state-of-the-art RaRR techniques, especially when training data is severely insufficient.
BibTeX
@article{Lu_Wu_2024, title={Semi-supervised Review-Aware Rating Regression (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26996}, DOI={10.1609/aaai.v37i13.26996}, abstractNote={Semi-supervised learning is a promising solution to mitigate data sparsity in review-aware rating regression (RaRR), but it bears the risk of learning with noisy pseudo-labelled data. In this paper, we propose a paradigm called co-training-teaching (CoT2), which integrates the merits of both co-training and co-teaching towards the robust semi-supervised RaRR. Concretely, CoT2 employs two predictors and each of them alternately plays the roles of "labeler" and "validator" to generate and validate pseudo-labelled instances. Extensive experiments show that CoT2 considerably outperforms state-of-the-art RaRR techniques, especially when training data is severely insufficient.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lu, Xiangkui and Wu, Jun}, year={2024}, month={Jul.}, pages={16272-16273} }