  Human conversations of recommendation naturally involve the shift of
interests which can align the recommendation actions and conversation process
to make accurate recommendations with rich explanations. However, existing
conversational recommendation systems (CRS) ignore the advantage of user
interest shift in connecting recommendation and conversation, which leads to an
ineffective loose coupling structure of CRS. To address this issue, by modeling
the recommendation actions as recommendation paths in a knowledge graph (KG),
we propose DICR (Dual Imitation for Conversational Recommendation), which
designs a dual imitation to explicitly align the recommendation paths and user
interest shift paths in a recommendation module and a conversation module,
respectively. By exchanging alignment signals, DICR achieves bidirectional
promotion between recommendation and conversation modules and generates
high-quality responses with accurate recommendations and coherent explanations.
Experiments demonstrate that DICR outperforms the state-of-the-art models on
recommendation and conversation performance with automatic, human, and novel
explainability metrics.
