论文标题
CR-Walker：树结构的图形推理和对话框进行对话推荐
CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation
论文作者
论文摘要
对话推荐系统（CRS）吸引了日益增长的兴趣，该系统通过对话互动来探索用户偏好，以便提出适当的建议。但是，现有CRS仍缺乏能力（1）（1）在背景知识上遍历多个推理路径来引入相关项目和属性，并且（2）在当前系统的目的中适当地安排选定的实体以控制响应生成。为了解决这些问题，我们在本文中提出了CR-Walker，该模型在知识图上执行树结构的推理，并生成内容丰富的对话框行为以指导语言生成。树结构推理的独特方案视为对话框的一部分在每个跳跃中遍历的实体，以促进语言生成，这将选择和表达实体的方式链接。自动和人类评估表明，CR-Walker可以提出更准确的建议，并产生更有信息和引人入胜的反应。
Growing interests have been attracted in Conversational Recommender Systems (CRS), which explore user preference through conversational interactions in order to make appropriate recommendation. However, there is still a lack of ability in existing CRS to (1) traverse multiple reasoning paths over background knowledge to introduce relevant items and attributes, and (2) arrange selected entities appropriately under current system intents to control response generation. To address these issues, we propose CR-Walker in this paper, a model that performs tree-structured reasoning on a knowledge graph, and generates informative dialog acts to guide language generation. The unique scheme of tree-structured reasoning views the traversed entity at each hop as part of dialog acts to facilitate language generation, which links how entities are selected and expressed. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation, and generate more informative and engaging responses.
