  Proactively and naturally guiding the dialog from the non-recommendation
context (e.g., Chit-chat) to the recommendation scenario (e.g., Music) is
crucial for the Conversational Recommender System (CRS). Prior studies mainly
focus on planning the next dialog goal~(e.g., chat on a movie star) conditioned
on the previous dialog. However, we find the dialog goals can be simultaneously
observed at different levels, which can be utilized to improve CRS. In this
paper, we propose Dual-space Hierarchical Learning (DHL) to leverage
multi-level goal sequences and their hierarchical relationships for
conversational recommendation. Specifically, we exploit multi-level goal
sequences from both the representation space and the optimization space. In the
representation space, we propose the hierarchical representation learning where
a cross attention module derives mutually enhanced multi-level goal
representations. In the optimization space, we devise the hierarchical weight
learning to reweight lower-level goal sequences, and introduce bi-level
optimization for stable update. Additionally, we propose a soft labeling
strategy to guide optimization gradually. Experiments on two real-world
datasets verify the effectiveness of our approach. Code and data are available
here.
