Reinforced Target-driven Conversational Promotion

Huy Quang Dao, Lizi Liao, Dung D. Le, Yuxiang Nie

Submission Type: Regular Long Paper

Submission Track: Dialogue and Interactive Systems

Keywords: Conversational Promotion, Conversational Recommendation, Target-driven Recommenders

TL;DR: In this work, we focus on a challenging setting called target-driven conversational promotion and introduce RTCP - a novel reinforced promotion framework.

Abstract: 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. RTCP integrates short-term and long-term planning via a balanced gating mechanism. Inside which, the dialogue actions are predicted via a knowledge-integrated multi-head attention and guided via reinforcement learning rewards. RTCP then employs action-guided prefix tuning 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.

Submission Number: 1830