论文标题
利用历史互动数据来改善对话推荐系统
Leveraging Historical Interaction Data for Improving Conversational Recommender System
论文作者
论文摘要
最近，对话推荐系统（CRS）已成为一个新兴和实用的研究主题。现有的大多数CRS方法都集中在学习有效的偏好表示形式上，仅对对话数据中的用户提供了有效的偏好表示。同时，我们采用新的观点来利用历史交互数据来改善CRS。为此，我们提出了一种新型的预训练方法，以通过预训练方法（来自对话数据）（来自对话数据）（来自历史交互数据）和基于属性的偏好序列（来自历史相互作用数据）进行整合。我们仔细设计了两个预训练任务，以增强基于项目和基于属性的偏好之间的信息融合。为了提高学习绩效，我们进一步开发了一个有效的负样品发生器，该生成器可以产生高质量的负样本。两个现实世界数据集的实验结果证明了我们改善CRS方法的有效性。
Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone. While, we take a new perspective to leverage historical interaction data for improving CRS. For this purpose, we propose a novel pre-training approach to integrating both item-based preference sequence (from historical interaction data) and attribute-based preference sequence (from conversation data) via pre-training methods. We carefully design two pre-training tasks to enhance information fusion between item- and attribute-based preference. To improve the learning performance, we further develop an effective negative sample generator which can produce high-quality negative samples. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach for improving CRS.
