论文标题
EGCR：会话推荐的解释生成
EGCR: Explanation Generation for Conversational Recommendation
论文作者
论文摘要
对话推荐系统（CRS）的注意力日益增长，该系统可作为基于对话和建议的以任务为基础的工具，以提供感兴趣的项目并探索用户偏好。但是，CRS中现有的工作未能向用户明确显示推理逻辑，并且整个CRS仍然是黑匣子。因此，我们提出了一个新颖的端到端框架，该框架名为“解释产生”，以基于对会话代理的解释，以解释其为何采取行动，以说明对话推荐（EGCR）（EGCR）。 EGCR结合了用户评论，以增强项目表示形式并提高整个对话的信息。据我们所知，这是对现实世界数据集上可解释的会话建议的第一个框架。此外，我们在一个基准的对话推荐数据集上评估了EGCR，并且比其他最先进的模型在建议准确性和对话质量方面取得更好的性能。最后，广泛的实验表明，生成的解释不仅具有高质量和解释性，而且使CRS更加值得信赖。我们将使我们的代码可为CRS社区做出贡献
Growing attention has been paid in Conversational Recommendation System (CRS), which works as a conversation-based and recommendation task-oriented tool to provide items of interest and explore user preference. However, existing work in CRS fails to explicitly show the reasoning logic to users and the whole CRS still remains a black box. Therefore we propose a novel end-to-end framework named Explanation Generation for Conversational Recommendation (EGCR) based on generating explanations for conversational agents to explain why they make the action. EGCR incorporates user reviews to enhance the item representation and increase the informativeness of the whole conversation. To the best of our knowledge, this is the first framework for explainable conversational recommendation on real-world datasets. Moreover, we evaluate EGCR on one benchmark conversational recommendation datasets and achieve better performance on both recommendation accuracy and conversation quality than other state-of-the art models. Finally, extensive experiments demonstrate that generated explanations are not only having high quality and explainability, but also making CRS more trustworthy. We will make our code available to contribute to the CRS community
