Personalized Review Recommendation based on Implicit dimension mining
Bei Xu, Yifan Xu · 2024
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. 1 Introduction Online product reviews are referential because they reflect the experience of past users.Some studies (Ventre and Kolbe, 2020) have shown the impact of online reviews on new users' purchase intention.Therefore, recommending useful reviews is helpful for users as well as e-commerce websites.Current review recommendation techniques focus on review helpfulness prediction, in which a key step is to extract features from reviews and user † The authors have contributed equally to this work.requirements.Most features are extracted from the 35 textual content (Saumya et al., 2023), which mainly 36 includes: lexical, textual, readability, and others 37