Abstract
Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of “text + dimension”. The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding “text + dimension” is better than encoding “text” and “dimension” separately in review recommendation.- Anthology ID:
- 2024.naacl-short.8
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 86–91
- Language:
- URL:
- https://aclanthology.org/2024.naacl-short.8
- DOI:
- Cite (ACL):
- Bei Xu and Yifan Xu. 2024. Personalized Review Recommendation based on Implicit dimension mining. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 86–91, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Personalized Review Recommendation based on Implicit dimension mining (Xu & Xu, NAACL 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.naacl-short.8.pdf