Conversational recommendation systems (CRS) could acquire dynamic user preferences towards desired items through multi-round interactive dialogue. Previous CRS mainly focuses on the single conversation (subsession) that user quits after a successful recommendation, neglecting the common scenario where user has multiple conversations (multi-subsession) over a short period. Therefore, we propose a novel conversational recommendation scenario named Multi-Subsession Multi-round Conversational Recommendation (MSMCR), where user would still resort to CRS after several subsessions and might preserve vague interests, and system would proactively ask attributes to activate user interests in the current subsession. To fill the gap in this new CRS scenario, we devise a novel framework called Multi-Subsession Conversational Recommender with Activation Attributes (MSCAA). Specifically, we first develop a context-aware recommendation module, comprehensively modeling user interests from historical interactions, previous subsessions, and feedback in the current subsession. Furthermore, an attribute selection policy module is proposed to learn a flexible strategy for asking appropriate attributes to elicit user interests. Finally, we design a conversation policy module to manage the above two modules to decide actions between asking and recommending. Extensive experiments on four datasets verify the effectiveness of our MSCAA framework for the MSMCR setting.

翻译：对话式推荐系统（CRS）能够通过多轮交互对话获取用户对目标物品的动态偏好。现有CRS主要关注单次对话（子会话）场景，即用户在成功推荐后退出，忽略了用户在短时间内进行多次对话（多子会话）的常见场景。为此，我们提出一种新型对话式推荐场景——多子会话多轮对话推荐（MSMCR），其中用户在经历若干子会话后仍会求助于CRS，可能保留模糊的兴趣偏好，而系统会在当前子会话中主动询问属性以激活用户兴趣。为填补这一新CRS场景的研究空白，我们设计了一种名为基于激活属性的多子会话对话式推荐器（MSCAA）的新型框架。具体而言，我们首先开发了上下文感知推荐模块，从历史交互、先前子会话及当前子会话反馈中全面建模用户兴趣。其次，提出属性选择策略模块，学习灵活策略以询问恰当属性来激发用户兴趣。最后，设计对话策略模块管理上述两个模块，决定询问与推荐之间的操作。在四个数据集上的大量实验验证了MSCAA框架在MSMCR设定下的有效性。