Sticking with a Winning Team: Better Neighbour Selection for Conversational Collaborative Recommendation

Conversational recommender systems have recently emerged
as useful alternative strategies to their single-shot counterpart, especially
given their ability to expose a user’s current preferences. These systems
use conversational feedback to hone in on the most suitable item for
recommendation by improving the mechanism that finds useful collaborators.
We propose a novel architecture for performing recommendation
that incorporates information about the individual performance of
neighbours during a recommendation session, into the neighbour retrieval
mechanism. We present our architecture and a set of preliminary evaluation
results that suggest there is some merit to our approach.We examine
these results and discuss what they mean for future research.