Explanations in conventional recommender systems have demonstrated benefits in helping the user understand the rationality of the recommendations and improving the system's efficiency, transparency, and trustworthiness. In the conversational environment, multiple contextualized explanations need to be generated, which poses further challenges for explanations. To better measure explainability in conversational recommender systems (CRS), we propose ten evaluation perspectives based on concepts from conventional recommender systems together with the characteristics of CRS. We assess five existing CRS benchmark datasets using these metrics and observe the necessity of improving the explanation quality of CRS. To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with over 2,000 high-quality rewritten explanations. We compare two baseline approaches to perform explanation generation based on E-ReDial. Experimental results suggest that models trained on E-ReDial can significantly improve explainability while introducing knowledge into the models can further improve the performance. GPT-3 in the in-context learning setting can generate more realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial can better generate clear reasons for recommendations based on user preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial.

翻译：传统推荐系统中的解释已被证明有助于用户理解推荐的合理性，并提升系统的效率、透明度和可信度。在对话环境中，需要生成多个情境化的解释，这给解释带来了进一步挑战。为了更好地衡量对话式推荐系统（CRS）中的可解释性，我们基于传统推荐系统的概念并结合CRS的特点，提出了十个评估视角。我们使用这些指标评估了五个现有的CRS基准数据集，观察到提升CRS解释质量的必要性。为此，我们采用手动和自动方法扩展这些对话，并构建了一个新的CRS数据集，即可解释推荐对话（E-ReDial）。该数据集包含756段对话，以及超过2000个高质量的重写解释。我们比较了两种基线方法基于E-ReDial进行解释生成。实验结果表明，在E-ReDial上训练的模型能显著提升可解释性，而将知识引入模型可进一步提高性能。在上下文学习设置下的GPT-3能生成更真实、更多样化的电影描述，而基于E-ReDial训练的T5能更好地根据用户偏好生成清晰的推荐理由。E-ReDial可在https://github.com/Superbooming/E-ReDial获取。