Recommenadation aided Caching using Combinatorial Multi-armed Bandits (2405.00080v3)

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.

• The paper presents a novel UCB-based algorithm that integrates recommendations with caching decisions using a CMAB framework.
• It derives upper regret bounds and demonstrates improved cache hit ratios compared to traditional greedy and basic CMAB-UCB methods.
• The approach leverages user behavior to inform real-time caching decisions, promising scalability in dynamic wireless networks.

Recommendation Aided Caching using Combinatorial Multi-armed Bandits

The paper introduces a novel approach to content caching in wireless networks, employing recommendation mechanisms and formalizing the problem using the framework of Combinatorial Multi-armed Bandits (CMAB). The authors propose an Upper Confidence Bound (UCB)-based algorithm to enhance cache hit rates by simultaneously deciding which contents to cache and recommend. Through rigorous analysis, an upper bound on the regret of the proposed algorithm is derived, highlighting its efficiency compared to existing approaches.

With the rapid proliferation of smart devices and intensive data applications, networks face an overwhelming surge in traffic characterized by redundant data. The authors' approach to caching leverages recommendations to alter user behavior, thus effectively directing requests towards cached content. This combined recommendation and caching strategy addresses inefficiencies in traditional approaches which independently control these mechanisms. By modeling each content as an arm within a CMAB, the framework allows for optimal decision-making, taking into account both content popularity and user preferences—both of which are initially unknown.

The innovative methodology presented demonstrates significant improvements in cache hit ratios. A detailed evaluation against state-of-the-art methods, including traditional greedy algorithms and basic CMAB-UCB approaches, underscores the superiority of the proposed UCB-based solution in minimizing regret over time. Numerical results validate the effectiveness of the recommendation-augmented caching approach, with scenarios exploring uniform and Zipf distribution models for recommendation-induced user preference.

The authors provide an intricate regret analysis, showing that as more information is gathered, their algorithm effectively learns optimal caching and recommendation strategies. The tight regret bounds hinge on sophisticated confidence intervals that account for varying levels of user acceptance of recommendations, making the algorithm robust against uncertainties in user behavior modeling.

Future implications of this research are substantial. The integration of recommendation systems into caching operations is an emerging paradigm that could redefine network management strategies, especially as content consumption patterns become more complex. The framework positions itself well for scalability and adaptation to various network contexts, including highly dynamic environments with fluctuating user interest profiles.

Further research could explore adaptive mechanisms where user acceptance rates ($w_u^{\text{rec}}$) are not static, expanding the model's flexibility. The inclusion of recommendation quality measures to better align with user interests offers yet another avenue for improvement. Moreover, the adaptation of the proposed solution to distributed network architectures and multi-tier caching scenarios promises fruitful exploration.

In summary, this paper provides a comprehensive approach to improving caching strategies in wireless networks through the synergistic use of recommendation systems. The combinatorial use of CMAB and UCB methodologies enhances decision-making capabilities, reducing system regret while improving user satisfaction. As the proliferation of data-driven services continues to grow, such advancements are critical in fostering more efficient and robust network infrastructure.