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We have developed a conversational recommendation system designed to helpusers navigate through a set of limited options to find the best choice. Unlikemany internet scale systems that use a singular set of search terms and returna ranked list of options from amongst thousands, our system uses multi-turnuser dialog to deeply understand the users preferences. The system responds incontext to the users specific and immediate feedback to make sequentialrecommendations. We envision our system would be highly useful in situationswith intrinsic constraints, such as finding the right restaurant within walkingdistance or the right retail item within a limited inventory. Our researchprototype instantiates the former use case, leveraging real data from GooglePlaces, Yelp, and Zomato. We evaluated our system against a similar system thatdid not incorporate user feedback in a 16 person remote study, generating 64scenario-based search journeys. When our recommendation system was successfullytriggered, we saw both an increase in efficiency and a higher confidence ratingwith respect to final user choice. We also found that users preferred oursystem (75%) compared with the baseline.

Developing a Conversational Recommendation System for Navigating Limited Options