  Conversational recommendation systems (CRS) aim to recommend suitable items
to users through natural language conversation. However, most CRS approaches do
not effectively utilize the signal provided by these conversations. They rely
heavily on explicit external knowledge e.g., knowledge graphs to augment the
models' understanding of the items and attributes, which is quite hard to
scale. To alleviate this, we propose an alternative information retrieval
(IR)-styled approach to the CRS item recommendation task, where we represent
conversations as queries and items as documents to be retrieved. We expand the
document representation used for retrieval with conversations from the training
set. With a simple BM25-based retriever, we show that our task formulation
compares favorably with much more complex baselines using complex external
knowledge on a popular CRS benchmark. We demonstrate further improvements using
user-centric modeling and data augmentation to counter the cold start problem
for CRSs.
