Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present A\c{C}AI, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity.

翻译：类似性搜索是多媒体检索系统和建议系统的关键操作,对于未来的机器学习和扩大现实应用来说也将起到重要作用。当这些系统需要为大型物体服务且有严格的延迟限制时,接近终端用户的边缘服务器可以作为相似性缓存运行,以加快检索速度。在本文中,我们介绍A\c{C}AI,一种新的相似性缓存政策,它通过(一) 整个目录的(近似)索引,决定哪些物体要在当地服务,哪些要从远程服务器检索,来改进最新版的本地物体,即使请求过程没有显示出任何统计规律性。