  Conversational recommender systems (CRS) utilize natural language
interactions and dialogue history to infer user preferences and provide
accurate recommendations. Due to the limited conversation context and
background knowledge, existing CRSs rely on external sources such as knowledge
graphs to enrich the context and model entities based on their inter-relations.
However, these methods ignore the rich intrinsic information within entities.
To address this, we introduce the Knowledge-Enhanced Entity Representation
Learning (KERL) framework, which leverages both the knowledge graph and a
pre-trained language model to improve the semantic understanding of entities
for CRS. In our KERL framework, entity textual descriptions are encoded via a
pre-trained language model, while a knowledge graph helps reinforce the
representation of these entities. We also employ positional encoding to
effectively capture the temporal information of entities in a conversation. The
enhanced entity representation is then used to develop a recommender component
that fuses both entity and contextual representations for more informed
recommendations, as well as a dialogue component that generates informative
entity-related information in the response text. A high-quality knowledge graph
with aligned entity descriptions is constructed to facilitate our study, namely
the Wiki Movie Knowledge Graph (WikiMKG). The experimental results show that
KERL achieves state-of-the-art results in both recommendation and response
generation tasks.
