Long Short-Term Planning for Conversational Recommendation Systems
Xian Li Hongguang Shi Yunfei Wang Yeqin Zhang Xubin Li Cam-Tu Nguyen
In Conversational Recommendation Systems (CRS), the central question is how
the conversational agent can naturally ask for user preferences and provide
suitable recommendations. Existing works mainly follow the hierarchical
architecture, where a higher policy decides whether to invoke the conversation
module (to ask questions) or the recommendation module (to make
recommendations). This architecture prevents these two components from fully
interacting with each other. In contrast, this paper proposes a novel
architecture, the long short-term feedback architecture, to connect these two
essential components in CRS. Specifically, the recommendation predicts the
long-term recommendation target based on the conversational context and the
user history. Driven by the targeted recommendation, the conversational model
predicts the next topic or attribute to verify if the user preference matches
the target. The balance feedback loop continues until the short-term planner
output matches the long-term planner output, that is when the system should
make the recommendation.