  There has been growing interest in using neural networks and deep learning
techniques to create dialogue systems. Conversational recommendation is an
interesting setting for the scientific exploration of dialogue with natural
language as the associated discourse involves goal-driven dialogue that often
transforms naturally into more free-form chat. This paper provides two
contributions. First, until now there has been no publicly available
large-scale dataset consisting of real-world dialogues centered around
recommendations. To address this issue and to facilitate our exploration here,
we have collected ReDial, a dataset consisting of over 10,000 conversations
centered around the theme of providing movie recommendations. We make this data
available to the community for further research. Second, we use this dataset to
explore multiple facets of conversational recommendations. In particular we
explore new neural architectures, mechanisms, and methods suitable for
composing conversational recommendation systems. Our dataset allows us to
systematically probe model sub-components addressing different parts of the
overall problem domain ranging from: sentiment analysis and cold-start
recommendation generation to detailed aspects of how natural language is used
in this setting in the real world. We combine such sub-components into a
full-blown dialogue system and examine its behavior.
