In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic reachability to quantify the maximum probability of recommending a target piece of content to an user for a set of allowable strategic modifications. This framework allows us to compute an upper bound on the likelihood of recommendation with minimal assumptions about user behavior. Stochastic reachability can be used to detect biases in the availability of content and diagnose limitations in the opportunities for discovery granted to users. We show that this metric can be computed efficiently as a convex program for a variety of practical settings, and further argue that reachability is not inherently at odds with accuracy. We demonstrate evaluations of recommendation algorithms trained on large datasets of explicit and implicit ratings. Our results illustrate how preference models, selection rules, and user interventions impact reachability and how these effects can be distributed unevenly.

中文翻译：

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通过随机可达性量化推荐系统中的可用性和发现

在这项工作中，我们考虑了交互式推荐系统中的偏好模型如何确定内容的可用性和用户的发现机会。我们提出了一种基于随机可达性的评估程序，以量化向用户推荐目标内容的最大概率，以进行一组允许的战略修改。该框架允许我们以对用户行为的最小假设来计算推荐可能性的上限。随机可达性可用于检测内容可用性的偏差，并诊断授予用户的发现机会的限制。我们表明，该度量可以作为各种实际设置的凸程序有效计算，并进一步论证可达性与准确性本质上并不矛盾。我们展示了在显式和隐式评分的大型数据集上训练的推荐算法的评估。我们的结果说明了偏好模型、选择规则和用户干预如何影响可达性以及这些影响如何分布不均。