Graph-Grounded Goal Planning for Conversational Recommendation
Zeming Liu, Ding Zhou, Hao Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, Ting Liu, Hui Xiong
Abstract
Conversational recommendation casts the recommendation problem as a dialog-based interactive task, which could acquire user interest more efficiently and effectively by allowing users to express what they like. In this work, we move a step towards a new conversational recommendation task that is more suitable for real-world applications. In this task, the recommender proactively and naturally lead a dialog from non-recommendation content to approach an item being of interest to users, and allow users to ask questions for better support of user decisions. The challenge of this task lies in how to effectively control the dialog flow to complete the recommendation while appropriately responding to user utterances. To address this challenge, we first construct a Chinese recommendation dialog dataset DuRecDial. We then propose a two-stage Multi-Goal driven Conversation Generation framework, MGCG. In particular, the goal planning module leverages the global graph structure information and local goal-sequence information to effectively control the dialog flow step by step. The goal-guided responding module can produce an in-depth dialog about each goal by fully exploiting hierarchical goal information for response retrieval or generation. Results on DuRecDial demonstrate that MGCG can lead the dialog more proactively and naturally, and complete the recommendation task more effectively.