  Recommender systems are ubiquitous yet often difficult for users to control,
and adjust if recommendation quality is poor. This has motivated conversational
recommender systems (CRSs), with control provided through natural language
feedback. However, as with most application domains, building robust CRSs
requires training data that reflects system usage$\unicode{x2014}$here
conversations with user utterances paired with items that cover a wide range of
preferences. This has proved challenging to collect scalably using conventional
methods. We address the question of whether it can be generated synthetically,
building on recent advances in natural language. We evaluate in the setting of
item set recommendation, noting the increasing attention to this task motivated
by use cases like music, news, and recipe recommendation. We present
TalkTheWalk, which synthesizes realistic high-quality conversational data by
leveraging domain expertise encoded in widely available curated item
collections, generating a sequence of hypothetical yet plausible item sets,
then using a language model to produce corresponding user utterances. We
generate over one million diverse playlist curation conversations in the music
domain, and show these contain consistent utterances with relevant item sets
nearly matching the quality of an existing but small human-collected dataset
for this task. We demonstrate the utility of the generated synthetic dataset on
a conversational item retrieval task and show that it improves over both
unsupervised baselines and systems trained on a real dataset.
