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Caching has been successfully applied in wired networks, in the context ofContent Distribution Networks (CDNs), and is quickly gaining ground forwireless systems. Storing popular content at the edge of the network (e.g. atsmall cells) is seen as a `win-win' for both the user (reduced access latency)and the operator (reduced load on the transport network and core servers).Nevertheless, the much smaller size of such edge caches, and the volatility ofuser preferences suggest that standard caching methods do not suffice in thiscontext. What is more, simple popularity-based models commonly used (e.g. IRM)are becoming outdated, as users often consume multiple contents in sequence(e.g. YouTube, Spotify), and this consumption is driven by recommendationsystems. The latter presents a great opportunity to bias the recommender tominimize content access cost (e.g. maximizing cache hit rates). To this end, inthis paper we first propose a Markovian model for recommendation-driven userrequests. We then formulate the problem of biasing the recommendation algorithmto minimize access cost, while maintaining acceptable recommendation quality.We show that the problem is non-convex, and propose an iterative ADMM-basedalgorithm that outperforms existing schemes, and shows significant potentialfor performance improvement on real content datasets.

Show me the Cache: Optimizing Cache-Friendly Recommendations for Sequential Content Access