A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions

TL;DR

This paper introduces learned beta distributions as a simple, accurate, and computationally efficient method for modeling confidence in recommender systems, enabling better insights without sacrificing performance.

Contribution

It proposes a novel approach using beta distributions for confidence estimation in RecSys, balancing simplicity, accuracy, and computational efficiency.

Findings

LBD maintains competitive accuracy with existing methods.

LBD shows a stronger correlation between confidence and accuracy.

Higher performance in high-precision targeted recommendations.

Abstract

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.