  The traditional recommendation systems mainly use offline user data to train
offline models, and then recommend items for online users, thus suffering from
the unreliable estimation of user preferences based on sparse and noisy
historical data. Conversational Recommendation System (CRS) uses the
interactive form of the dialogue systems to solve the intrinsic problems of
traditional recommendation systems. However, due to the lack of contextual
information modeling, the existing CRS models are unable to deal with the
exploitation and exploration (E&E) problem well, resulting in the heavy burden
on users. To address the aforementioned issue, this work proposes a contextual
information enhancement model tailored for CRS, called Knowledge Graph-enhanced
Sampling (KGenSam). KGenSam integrates the dynamic graph of user interaction
data with the external knowledge into one heterogeneous Knowledge Graph (KG) as
the contextual information environment. Then, two samplers are designed to
enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining
user preferences and reliable negative samples for updating recommender to
achieve efficient acquisition of user preferences and model updating, and thus
provide a powerful solution for CRS to deal with E&E problem. Experimental
results on two real-world datasets demonstrate the superiority of KGenSam with
significant improvements over state-of-the-art methods.
