A Learning-based Approach to Joint Content Caching and Recommendation at Base Stations
This work addresses the problem of efficient content delivery in wireless networks for network operators, but it is incremental as it builds on existing caching and recommendation techniques.
The paper tackles the joint optimization of content caching and recommendation at base stations to maximize caching gain while preserving user preferences, proposing a hierarchical iterative algorithm for known thresholds and an ε-greedy learning-based approach for unknown thresholds. Simulation results show the algorithms improve successful offloading probability compared to prior methods, with the ε-greedy algorithm achieving over 1-ε of the performance of the known-threshold algorithm.
Recommendation system is able to shape user demands, which can be used for boosting caching gain. In this paper, we jointly optimize content caching and recommendation at base stations to maximize the caching gain meanwhile not compromising the user preference. We first propose a model to capture the impact of recommendation on user demands, which is controlled by a user-specific psychological threshold. We then formulate a joint caching and recommendation problem maximizing the successful offloading probability, which is a mixed integer programming problem. We develop a hierarchical iterative algorithm to solve the problem when the threshold is known. Since the user threshold is unknown in practice, we proceed to propose an $\varepsilon$-greedy algorithm to find the solution by learning the threshold via interactions with users. Simulation results show that the proposed algorithms improve the successful offloading probability compared with prior works with/without recommendation. The $\varepsilon$-greedy algorithm learns the user threshold quickly, and achieves more than $1-\varepsilon$ of the performance obtained by the algorithm with known threshold.