Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations.Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues.It often yields responses with a limited perspective, confined to the scope of individual conversations.Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes.This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars?(2) How to effectively leverage these exemplars to enhance CRS models?To tackle these challenges, we introduce a novel Demonstrationenhanced Conversational Recommender System (DCRS), which aims to strengthen its understanding on the given dialogue contexts by retrieving and learning from demonstrations.In particular, we first propose a knowledge-aware contrastive learning method that adeptly taps into the mentioned entities and the dialogue's contextual essence for pretraining the demonstration retriever.Subsequently, we further develop two adaptive demonstrationaugmented prompt learning approaches, involving contextualized prompt learning and knowledge-enriched prompt learning, to bridge the gap between the retrieved demonstrations and the two end tasks of CRS, i.e., response generation and item recommendation, respectively.Rigorous evaluations on two established benchmark datasets underscore DCRS's superior performance over existing CRS methods in both item recommendation and response generation 1 .

The ability to proactively engage with users towards pitching products is highly desired for conversational assistants.However, existing conversational recommendation methods overemphasize on acquiring user preferences while ignore the strategic planning for nudging users towards accepting a designated item.Hence, these methods fail to promote specified items with engaging responses.In this work, we propose a Reinforced Target-driven Conversational Promotion (RTCP) framework for conversational promotion.Specifically, RTCP integrates short-term and long-term planning via a balanced gating mechanism.Inside which, the dialogue strategies are predicted via knowledge-integrated multi-head attention and guided via reinforcement learning rewards.RTCP then employs an action-guided prefix tuning method to generate relevant responses.Experimental results demonstrate that our model outperforms state-of-the-art models on both automatic metrics and human evaluation.Moreover, RTCP has a strong capability in quickly adapting to unseen scenarios just by updating prefix parameters without re-training the whole model.Code and data are here 1 .