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

TL;DR

This paper introduces a Markovian model for recommendation-driven user requests and proposes an ADMM-based algorithm to optimize cache-friendly recommendations, significantly reducing access costs in edge caching scenarios.

Contribution

It presents a novel Markovian request model and an optimization algorithm to bias recommendations for improved cache efficiency, addressing limitations of traditional popularity-based caching.

Findings

The proposed algorithm outperforms existing schemes in simulations.

Significant reduction in content access costs demonstrated on real datasets.

Effective biasing of recommendations enhances cache hit rates.

Abstract

Caching has been successfully applied in wired networks, in the context of Content Distribution Networks (CDNs), and is quickly gaining ground for wireless systems. Storing popular content at the edge of the network (e.g. at small 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 of user preferences suggest that standard caching methods do not suffice in this context. What is more, simple popularity-based models commonly used (e.g. IRM) are becoming outdated, as users often consume multiple contents in sequence (e.g. YouTube, Spotify), and this consumption is driven by recommendation systems. The latter presents a great opportunity to bias the recommender to minimize content access cost (e.g. maximizing cache hit rates). To this end, in this paper we first propose a Markovian model for recommendation-driven user requests. We then formulate the problem of biasing the recommendation algorithm to minimize access cost, while maintaining acceptable recommendation quality. We show that the problem is non-convex, and propose an iterative ADMM-based algorithm that outperforms existing schemes, and shows significant potential for performance improvement on real content datasets.