In Conversational Recommendation System (CRS), an agent is asked to recommend a set of items to users within natural language conversations. To address the need for both conversational capability and personalized recommendations, prior works have utilized separate recommendation and dialogue modules. However, such approach inevitably results in a discrepancy between recommendation results and generated responses. To bridge the gap, we propose a multi-task learning for a unified CRS, where a single model jointly learns both tasks via Contextualized Knowledge Distillation (ConKD). We introduce two versions of ConKD: hard gate and soft gate. The former selectively gates between two task-specific teachers, while the latter integrates knowledge from both teachers. Our gates are computed on-the-fly in a context-specific manner, facilitating flexible integration of relevant knowledge. Extensive experiments demonstrate that our single model significantly improves recommendation performance while enhancing fluency, and achieves comparable results in terms of diversity.

翻译：在对话式推荐系统（CRS）中，智能体需在自然语言对话中向用户推荐一组项目。为兼顾对话能力与个性化推荐需求，先前工作采用了分离的推荐模块与对话模块。然而，此类方法不可避免地导致推荐结果与生成回复之间存在差异。为弥合这一差距，我们提出了一种面向统一CRS的多任务学习方法，其中单一模型通过上下文知识蒸馏（ConKD）联合学习两个任务。我们引入了两种ConKD变体：硬门控与软门控。前者在两种任务专用教师模型之间选择性切换，后者则融合两个教师模型的输出。我们的门控机制以上下文特定方式动态计算，从而支持对相关知识的灵活整合。大量实验表明，我们的单一模型在显著提升推荐性能的同时增强了回复流畅性，并在多样性指标上取得了可比结果。