Recommenadation aided Caching using Combinatorial Multi-armed Bandits

TL;DR

This paper introduces a novel approach to content caching in wireless networks by integrating recommendations and employing combinatorial multi-armed bandit algorithms to optimize cache hits under uncertainty.

Contribution

It formulates the cache optimization problem as a CMAB and develops UCB-based algorithms for both known and unknown user preferences, advancing caching strategies with recommendation integration.

Findings

Proposed algorithms outperform existing methods in simulations.

Effectively learns user preferences and recommendation acceptabilities.

Achieves higher cache hit rates with adaptive learning.

Abstract

We study content caching with recommendations in a wireless network where the users are connected through a base station equipped with a finite-capacity cache. We assume a fixed set of contents with unknown user preferences and content popularities. The base station can cache a subset of the contents and can also recommend subsets of the contents to different users in order to encourage them to request the recommended contents. Recommendations, depending on their acceptability, can thus be used to increase cache hits. We first assume that the users' recommendation acceptabilities are known and formulate the cache hit optimization problem as a combinatorial multi-armed bandit (CMAB). We propose a UCB-based algorithm to decide which contents to cache and recommend and provide an upper bound on the regret of this algorithm. Subsequently, we consider a more general scenario where the users' recommendation acceptabilities are also unknown and propose another UCB-based algorithm that learns these as well. We numerically demonstrate the performance of our algorithms and compare these to state-of-the-art algorithms.