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Similarity search is a key operation in multimedia retrieval systems andrecommender systems, and it will play an important role also for future machinelearning and augmented reality applications. When these systems need to servelarge objects with tight delay constraints, edge servers close to the end-usercan operate as similarity caches to speed up the retrieval. In this paper wepresent A\c{C}AI, a new similarity caching policy which improves on the stateof the art by using (i) an (approximate) index for the whole catalog to decidewhich 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 withstrong guarantees even when the request process does not exhibit anystatistical regularity.

Ascent Similarity Caching with Approximate Indexes