  Conversational recommendation systems (CRS) aim to timely and proactively
acquire user dynamic preferred attributes through conversations for item
recommendation. In each turn of CRS, there naturally have two decision-making
processes with different roles that influence each other: 1) director, which is
to select the follow-up option (i.e., ask or recommend) that is more effective
for reducing the action space and acquiring user preferences; and 2) actor,
which is to accordingly choose primitive actions (i.e., asked attribute or
recommended item) that satisfy user preferences and give feedback to estimate
the effectiveness of the director's option. However, existing methods heavily
rely on a unified decision-making module or heuristic rules, while neglecting
to distinguish the roles of different decision procedures, as well as the
mutual influences between them. To address this, we propose a novel
Director-Actor Hierarchical Conversational Recommender (DAHCR), where the
director selects the most effective option, followed by the actor accordingly
choosing primitive actions that satisfy user preferences. Specifically, we
develop a dynamic hypergraph to model user preferences and introduce an
intrinsic motivation to train from weak supervision over the director. Finally,
to alleviate the bad effect of model bias on the mutual influence between the
director and actor, we model the director's option by sampling from a
categorical distribution. Extensive experiments demonstrate that DAHCR
outperforms state-of-the-art methods.
