  Recommendation systems focus on helping users find items of interest in the
situations of information overload, where users' preferences are typically
estimated by the past observed behaviors. In contrast, conversational
recommendation systems (CRS) aim to understand users' preferences via
interactions in conversation flows. CRS is a complex problem that consists of
two main tasks: (1) recommendation and (2) response generation. Previous work
often tried to solve the problem in a modular manner, where recommenders and
response generators are separate neural models. Such modular architectures
often come with a complicated and unintuitive connection between the modules,
leading to inefficient learning and other issues. In this work, we propose a
unified framework based on BART for conversational recommendation, which
tackles two tasks in a single model. Furthermore, we also design and collect a
lightweight knowledge graph for CRS in the movie domain. The experimental
results show that the proposed methods achieve the state-of-the-art performance
in terms of both automatic and human evaluation.
