We study the problem of coded caching with nonuniform file popularity under the setting where the popularity distribution is initially unknown. By reframing the problem, we propose a method inspired by an algorithm from the recommender-systems literature and multi-armed bandits. Unlike prior approaches, which focus on accurately estimating file popularities, our method ranks files relative to one another and partitions them into groups. This perspective is more consistent with the structure of prior approaches as well, since earlier methods also divided files into popular and non-popular groups after estimating their popularities. The proposed approach relies on differences in request counts between files as the basis for ranking, and under many conditions it outperforms the previous algorithm. In particular, we obtain significantly improved performance in scenarios where the number of users in the network is small, the cache storage capacity is limited, or the learning process of the true popularity of files based on observations is contaminated by exploratory or synthetic requests that do not match the true popularity distribution. In these cases, our policy achieves markedly better performance and attains sublinear regret.

翻译：本文研究在初始未知流行度分布条件下，具有非均匀文件流行度的编码缓存问题。通过重构问题框架，我们提出一种受推荐系统文献和多臂赌博机算法启发的方法。与先前侧重于准确估计文件流行度的方法不同，本方法对文件进行相对排序并将其划分为若干组。该视角与现有方法的结构更为一致，因为早期方法在估计文件流行度后同样会将文件划分为热门与非热门组。所提方法以文件间请求计数的差异作为排序依据，在多种条件下其性能优于现有算法。特别地，在网络用户数量较少、缓存存储容量有限，或基于观测的文件真实流行度学习过程受到与真实分布不匹配的探索性/合成请求干扰的场景中，本方法获得了显著性能提升。在这些情况下，我们的策略实现了明显更优的性能，并获得了次线性遗憾界。