Learning Neural Templates for Recommender Dialogue System

Understand

Though recent end-to-end neural models have shown promising progress on Conversational Recommender System (CRS), two key challenges still remain.

• First, the recommended items cannot be always incorporated into the generated replies precisely and appropriately.
• Second, only the items mentioned in the training corpus have a chance to be recommended in the conversation.
• To tackle these challenges, we introduce a novel framework called NTRD for recommender dialogue system that decouples the dialogue generation from the item recommendation.