  We have developed a conversational recommendation system designed to help
users navigate through a set of limited options to find the best choice. Unlike
many internet scale systems that use a singular set of search terms and return
a ranked list of options from amongst thousands, our system uses multi-turn
user dialog to deeply understand the users preferences. The system responds in
context to the users specific and immediate feedback to make sequential
recommendations. We envision our system would be highly useful in situations
with intrinsic constraints, such as finding the right restaurant within walking
distance or the right retail item within a limited inventory. Our research
prototype instantiates the former use case, leveraging real data from Google
Places, Yelp, and Zomato. We evaluated our system against a similar system that
did not incorporate user feedback in a 16 person remote study, generating 64
scenario-based search journeys. When our recommendation system was successfully
triggered, we saw both an increase in efficiency and a higher confidence rating
with respect to final user choice. We also found that users preferred our
system (75%) compared with the baseline.
