  Conversational recommender system is an emerging area that has garnered an
increasing interest in the community, especially with the advancements in large
language models (LLMs) that enable diverse reasoning over conversational input.
Despite the progress, the field has many aspects left to explore. The currently
available public datasets for conversational recommendation lack specific user
preferences and explanations for recommendations, hindering high-quality
recommendations. To address such challenges, we present a novel conversational
recommendation dataset named PEARL, synthesized with persona- and
knowledge-augmented LLM simulators. We obtain detailed persona and knowledge
from real-world reviews and construct a large-scale dataset with over 57k
dialogues. Our experimental results demonstrate that utterances in PEARL
include more specific user preferences, show expertise in the target domain,
and provide recommendations more relevant to the dialogue context than those in
prior datasets.
