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.

翻译：大多数推荐系统（RecSys）无法提供其决策的置信度指标，因此无法区分确定性推荐与非确定性推荐。现有针对推荐系统的置信度方法要么是不准确的启发式规则、概念复杂，要么计算成本高昂。因此，实际应用的推荐系统鲜少采用这些方法，其行为缺乏置信度洞察。本文提出基于学习贝塔分布（LBD）的简洁实用推荐方法，其内置显式置信度度量。核心洞见在于：贝塔分布将用户偏好预测为概率分布，天然可在闭区间内建模置信度，同时仅需最低模型复杂度即可实现。实验表明，LBD在保持与现有方法相当精度的同时，其精度与置信度的相关性显著更强。此外，LBD在高精度目标推荐任务中表现更优。本研究证明了在不牺牲简洁性与精度的前提下，推荐系统可实现置信度建模且无需引入沉重计算负担，有望为实际推荐系统提供更优洞察，并开拓新型应用场景。