1 research outputs found
A Bayesian Approach to Conversational Recommendation Systems
We present a conversational recommendation system based on a Bayesian
approach. A probability mass function over the items is updated after any
interaction with the user, with information-theoretic criteria optimally
shaping the interaction and deciding when the conversation should be terminated
and the most probable item consequently recommended. Dedicated elicitation
techniques for the prior probabilities of the parameters modeling the
interactions are derived from basic structural judgements. Such prior
information can be combined with historical data to discriminate items with
different recommendation histories. A case study based on the application of
this approach to \emph{stagend.com}, an online platform for booking
entertainers, is finally discussed together with an empirical analysis showing
the advantages in terms of recommendation quality and efficiency.Comment: Accepted for oral presentation at the \emph{AAAI 2020 Workshop on
Interactive and Conversational Recommendation Systems} (WICRS