  In this work, we present a new dataset for conversational recommendation over
knowledge graphs in e-commerce platforms called COOKIE. The dataset is
constructed from an Amazon review corpus by integrating both user-agent
dialogue and custom knowledge graphs for recommendation. Specifically, we first
construct a unified knowledge graph and extract key entities between
user--product pairs, which serve as the skeleton of a conversation. Then we
simulate conversations mirroring the human coarse-to-fine process of choosing
preferred items. The proposed baselines and experiments demonstrate that our
dataset is able to provide innovative opportunities for conversational
recommendation.
