发表机构
Toronto Metropolitan University（多伦多都会大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CLARER模型，结合评分与评论方面特征，利用Transformer和对比学习提升推荐准确性与可解释性，在三个基准数据集上优于基线。
AI 中文摘要
在这项工作中，我们提出了一种新颖的推荐模型CLARER（面向可解释推荐的方面表示对比学习），该模型将从文本评论中学习到的方面特征与评分信息相结合，以提高推荐的准确性和可解释性。我们提出的框架通过结合基于评分的特征和基于评论的方面特征来学习用户和物品表示。具体而言，基于评分的特征通过多层感知机（MLP）模型学习，而方面特定的评论表示则使用Transformer编码器来捕获语义信息，并通过对比学习更好地区分用户偏好。为了提供解释，我们训练了一个Transformer解码器，将来自评分和方面特征的用户和物品最终表示作为上下文。在三个基准数据集上的实验结果表明，我们的模型在推荐（准确性）和解释生成方面均优于基线方法。
英文摘要
In this work, we propose a novel recommendation model, CLARER (Contrastive Learning for Aspect Representation towards Explainable Recommendation) that integrates aspect features learned from textual reviews with rating information to improve the accuracy and explainability of recommendations. Our proposed framework learns user and item representations by combining rating-based features and aspect-based features from reviews. Specifically, rating-based features are learned through a multi-layer perceptron (MLP) model, while aspect-specific review representations are learned using a transformer encoder to capture the semantic information and contrastive learning to better distinguish user preferences. To provide explanations, we train a transformer decoder, using the final representations of users and items from both rating and aspect-based features as context. Experimental results in three benchmark data sets demonstrate that our model achieves superior performance compared to baseline methods in both recommendation (accuracy) and explanation generation.
Comments8 pages. Published in WI-IAT 2025. Best Student Paper Award
Journal refE. Hasan and C. Ding, "Contrastive Learning for Aspect Representation Towards Explainable Recommendation," 2025 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 483-490, 2025
DOI：10.1109/WI-IAT67162.2025.00070
