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.
Cite
Text
Lu and Wu. "Semi-Supervised Review-Aware Rating Regression (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26996Markdown
[Lu and Wu. "Semi-Supervised Review-Aware Rating Regression (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/lu2023aaai-semi/) doi:10.1609/AAAI.V37I13.26996BibTeX
@inproceedings{lu2023aaai-semi,
title = {{Semi-Supervised Review-Aware Rating Regression (Student Abstract)}},
author = {Lu, Xiangkui and Wu, Jun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {16272-16273},
doi = {10.1609/AAAI.V37I13.26996},
url = {https://mlanthology.org/aaai/2023/lu2023aaai-semi/}
}