Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences is of crucial importance for CRS. Previous works learn the user preferences with pairwise relations from the interactive conversation and item knowledge, while largely ignoring the fact that factors for a relationship in CRS are multiplex. Specifically, the user likes/dislikes the items that satisfy some attributes (Like/Dislike view). Moreover social influence is another important factor that affects user preference towards the item (Social view), while is largely ignored by previous works in CRS. The user preferences from these three views are inherently different but also correlated as a whole. The user preferences from the same views should be more similar than that from different views. The user preferences from Like View should be similar to Social View while different from Dislike View. To this end, we propose a novel model, namely Multi-view Hypergraph Contrastive Policy Learning (MHCPL). Specifically, MHCPL timely chooses useful social information according to the interactive history and builds a dynamic hypergraph with three types of multiplex relations from different views. The multiplex relations in each view are successively connected according to their generation order.

翻译：对话式推荐系统（CRS）旨在通过交互方式获取用户偏好，并据此向用户推荐物品。准确学习动态用户偏好对CRS至关重要。以往工作主要基于交互对话和物品知识中的成对关系学习用户偏好，但忽略了CRS中关系因素的多元性。具体而言，用户对满足某些属性的物品存在喜欢/不喜欢（喜欢/不喜欢视图），而社会影响作为影响用户对物品偏好的另一重要因素（社会视图），在以往CRS研究中常被忽略。这三个视图中的用户偏好本质上不同，但作为一个整体相互关联。同一视图中的用户偏好应比不同视图中的更为相似，且喜欢视图中的用户偏好应与社会视图相似，而与不喜欢视图不同。为此，我们提出了一种新颖模型——多视图超图对比策略学习（MHCPL）。具体地，MHCPL根据交互历史适时选择有用的社会信息，并构建一个包含三种不同视图多元关系的动态超图。每个视图中的多元关系按其生成顺序依次连接。