Abstract
User-generated contents’ score-based prediction and item recommendation has become an inseparable part of the online recommendation systems. The ratings allow people to express their opinions and may affect the market value of items and consumer confidence in e-commerce decisions. A major problem with the models designed for user review prediction is that they unknowingly neglect the rating bias occurring due to personal user bias preferences. We propose a tendency-based approach that models the user and item tendency for score prediction along with text review analysis with respect to ratings.- Anthology ID:
- 2020.aacl-srw.8
- Volume:
- Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Boaz Shmueli, Yin Jou Huang
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–56
- Language:
- URL:
- https://aclanthology.org/2020.aacl-srw.8
- DOI:
- Cite (ACL):
- Pranshi Yadav, Priya Yadav, Pegah Nokhiz, and Vivek Gupta. 2020. Unbiasing Review Ratings with Tendency Based Collaborative Filtering. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop, pages 50–56, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Unbiasing Review Ratings with Tendency Based Collaborative Filtering (Yadav et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2020.aacl-srw.8.pdf
- Code
- pranshiyadav06/review-bias-normalization