손지원/한보혜’s paper has been accepted in

Title: TEDI: Target-Review-based Knowledge Distillation for Effective Recommendation,
Author: Jiwon Son, Bohye Han, and Sang-Wook Kim
Abstract
Review-based recommendation systems (RBRS) leverage textual reviews to capture rich information about user preferences. A target review, which is directly associated with the target rating, contains highly relevant information for rating prediction but is unavailable during inference. Existing RBRS approaches try to reproduce the target review’s semantic embedding by leveraging historical reviews. However, this semantic embedding often includes ratingirrelevant information, limiting its effectiveness in rating prediction. To address this issue, we propose TEDI, a novel RBRS based on privileged feature distillation (PFD). TEDI consists of a teacher module that distills rating-relevant knowledge from both target and historical reviews, and a student module that reproduces this knowledge by leveraging historical reviews during inference. Experiments on six datasets demonstrate that TEDI outperforms nine state-of-the-art RBRS methods, validating the effectiveness of utilizing knowledge from target reviews. Our code and data are available at https://anonymous.4open.science/r/TEDI-2420.