Conversational recommender systems (CRS) aim to recommend high-quality items to users
through interactive conversations. Although several efforts have been made for CRS, two
major issues still remain to be solved. First, the conversation data itself lacks of sufficient
contextual information for accurately understanding users' preference. Second, there is a
semantic gap between natural language expression and item-level user preference. To
address these issues, we incorporate both word-oriented and entity-oriented knowledge …

Conversational recommender systems (CRS) use interactive discussions to recommend
high-quality items to users. Two essential components in a good CRS are the
recommendation module that makes pertinent product recommendations to consumers and
a conversation component that creates text-based sentences with product
recommendations. The most commonly used dataset to train CRS models is ReDial. In this
paper, we found that using the INSPIRED dataset in place of the ReDial dataset significantly …