Personalized review recommendation based on Implicit dimension mining

Anonymous

TL;DR: We propose a textual review recommendation model that significantly outperforms other state-of-the-art textual models, with some of the metrics outperforming the state-of-the-art multi-modal models.

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.

Paper Type: short

Research Area: Information Retrieval and Text Mining

Contribution Types: NLP engineering experiment

Languages Studied: English,Chinese