HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation
HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation
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DOI：
10.1145/3404835.3463247
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Zuohui Fu;Yikun Xian;Yaxin Zhu;Shuyuan Xu;Zelong Li;Gerard de Melo;Yongfeng Zhang
中科院分区：
文献类型：
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作者：
Zuohui Fu;Yikun Xian;Yaxin Zhu;Shuyuan Xu;Zelong Li;Gerard de Melo;Yongfeng Zhang
人们越来越认识到需要以人为本的AI，从人类反馈中学习。然而，目前大多数人工智能系统更多地关注模型设计，而较少关注人类参与管道的一部分。在这项工作中，我们提出了一个人在环（HitL）图推理范式，并开发了一个相应的数据集命名为HOOPS的KG驱动的会话推荐的任务。具体来说，我们首先构建一个KG解释不同的用户行为，并确定相关的属性实体为每个用户-项目对。然后，我们模拟会话轮反映人类的决策过程中选择合适的项目跟踪KG结构透明。我们还提供了一个基准方法与报告的数据集上的性能，以确定使用我们开发的数据集推荐HitL图推理的可行性，并表明它为研究界提供了新的机会。
There is increasing recognition of the need for human-centered AI that learns from human feedback. However, most current AI systems focus more on the model design, but less on human participation as part of the pipeline. In this work, we propose a Human-in-the-Loop (HitL) graph reasoning paradigm and develop a corresponding dataset named HOOPS for the task of KG-driven conversational recommendation. Specifically, we first construct a KG interpreting diverse user behaviors and identify pertinent attribute entities for each user--item pair. Then we simulate the conversational turns reflecting the human decision making process of choosing suitable items tracing the KG structures transparently. We also provide a benchmark method with reported performance on the dataset to ascertain the feasibility of HitL graph reasoning for recommendation using our developed dataset, and show that it provides novel opportunities for the research community.