用大模型生成更连贯的推荐解释，提升用户理解度。
Coherency Improved Explainable Recommendation via Large Language Model
- 用大模型先生成评分再转为向量，驱动解释生成
- 在三个数据集上解释力提升7.3%，文本质量提升4.4%
- 无需人工标注，自动评估解释与评分的一致性
可解释推荐系统旨在阐明每项推荐背后的逻辑，帮助用户理解。以往方法将评分预测与解释生成联合进行，但存在评分与解释不一致的问题。为此，我们提出一种新框架：利用大语言模型（LLM）生成评分，将其转化为评分向量，并基于该向量及用户-物品信息生成解释。此外，我们采用公开的大语言模型和预训练情感分析模型，无需人工标注即可自动评估解释与评分的连贯性。在三个可解释推荐数据集上的实验表明，所提框架有效，相比现有最优基线，在解释力上提升7.3%，文本质量提升4.4%。
原文摘要 · Abstract (English)
Explainable recommender systems are designed to elucidate the explanation behind each recommendation, enabling users to comprehend the underlying logic. Previous works perform rating prediction and explanation generation in a multi-task manner. However, these works suffer from incoherence between predicted ratings and explanations. To address the issue, we propose a novel framework that employs a large language model (LLM) to generate a rating, transforms it into a rating vector, and finally generates an explanation based on the rating vector and user-item information. Moreover, we propose utilizing publicly available LLMs and pre-trained sentiment analysis models to automatically evaluate the coherence without human annotations. Extensive experimental results on three datasets of explainable recommendation show that the proposed framework is effective, outperforming state-of-the-art baselines with improvements of 7.3\% in explainability and 4.4\% in text quality.
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