  Traditional recommender systems estimate user preference on items purely
based on historical interaction records, thus failing to capture fine-grained
yet dynamic user interests and letting users receive recommendation only
passively. Recent conversational recommender systems (CRSs) tackle those
limitations by enabling recommender systems to interact with the user to obtain
her/his current preference through a sequence of clarifying questions. Despite
the progress achieved in CRSs, existing solutions are far from satisfaction in
the following two aspects: 1) current CRSs usually require each user to answer
a quantity of clarifying questions before reaching the final recommendation,
which harms the user experience; 2) there is a semantic gap between the learned
representations of explicitly mentioned attributes and items. To address these
drawbacks, we introduce the knowledge graph (KG) as the auxiliary information
for comprehending and reasoning a user's preference, and propose a new CRS
framework, namely Knowledge Enhanced Conversational Reasoning (KECR) system. As
a user can reflect her/his preference via both attribute- and item-level
expressions, KECR closes the semantic gap between two levels by embedding the
structured knowledge in the KG. Meanwhile, KECR utilizes the connectivity
within the KG to conduct explicit reasoning of the user demand, making the
model less dependent on the user's feedback to clarifying questions. KECR can
find a prominent reasoning chain to make the recommendation explainable and
more rationale, as well as smoothen the conversation process, leading to better
user experience and conversational recommendation accuracy. Extensive
experiments on two real-world datasets demonstrate our approach's superiority
over state-of-the-art baselines in both automatic evaluations and human
judgments.
