Reinforced Target-driven Conversational Promotion

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 .