A Lightweight Method for Modeling Confidence in Recommendations with
  Learned Beta Distributions
Norman Knyazev Harrie Oosterhuis
Most Recommender Systems (RecSys) do not provide an indication of confidence
in their decisions. Therefore, they do not distinguish between recommendations
of which they are certain, and those where they are not. Existing confidence
methods for RecSys are either inaccurate heuristics, conceptually complex or
computationally very expensive. Consequently, real-world RecSys applications
rarely adopt these methods, and thus, provide no confidence insights in their
behavior. In this work, we propose learned beta distributions (LBD) as a simple
and practical recommendation method with an explicit measure of confidence. Our
main insight is that beta distributions predict user preferences as probability
distributions that naturally model confidence on a closed interval, yet can be
implemented with the minimal model-complexity. Our results show that LBD
maintains competitive accuracy to existing methods while also having a
significantly stronger correlation between its accuracy and confidence.
Furthermore, LBD has higher performance when applied to a high-precision
targeted recommendation task. Our work thus shows that confidence in RecSys is
possible without sacrificing simplicity or accuracy, and without introducing
heavy computational complexity. Thereby, we hope it enables better insight into
real-world RecSys and opens the door for novel future applications.