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.

翻译：主动且自然地引导对话从非推荐场景（如闲聊）过渡到推荐场景（如音乐）对于会话推荐系统（CRS）至关重要。现有研究主要关注基于先前对话规划下一个对话目标（例如，讨论某位影星）。然而，我们发现对话目标可在不同层级上同时被观测，这可用于改进CRS。本文提出双空间分层学习（Dual-space Hierarchical Learning, DHL），利用多层级目标序列及其层级关系进行会话推荐。具体而言，我们从表征空间和优化空间两个维度挖掘多层级目标序列。在表征空间中，我们提出分层表征学习，通过跨注意力模块生成相互增强的多层级目标表征。在优化空间中，我们设计分层权重学习对低层级目标序列进行重新加权，并引入双层优化以实现稳定更新。此外，我们提出软标签策略以逐步引导优化过程。在两个真实数据集上的实验验证了本方法有效性。代码与数据已公开。