  Recently, conversational recommender system (CRS) has become an emerging and
practical research topic. Most of the existing CRS methods focus on learning
effective preference representations for users from conversation data alone.
While, we take a new perspective to leverage historical interaction data for
improving CRS. For this purpose, we propose a novel pre-training approach to
integrating both item-based preference sequence (from historical interaction
data) and attribute-based preference sequence (from conversation data) via
pre-training methods. We carefully design two pre-training tasks to enhance
information fusion between item- and attribute-based preference. To improve the
learning performance, we further develop an effective negative sample generator
which can produce high-quality negative samples. Experiment results on two
real-world datasets have demonstrated the effectiveness of our approach for
improving CRS.
