A Learning-based Approach to Joint Content Caching and Recommendation at Base Stations
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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. We first propose a model to capture the impact of recommendation on user demands, which is controlled by a user-specific threshold. We then formulate a joint caching and recommendation problem maximizing the cache-hit ratio, which is NP-hard. To solve the problem efficiently, we develop a greedy algorithm. Since the user threshold is unknown in practice, we proceed to propose an ϵ-greedy algorithm to learn the threshold via interactions with users. Simulation results show that the greedy algorithm achieves near-optimal performance and improves the cache-hit ratio significantly compared with priori works with/without recommendation. The ϵ-greedy algorithm learns the user threshold quickly, and achieves more than 1-ϵ of the cache-hit ratio obtained by the greedy algorithm with known user threshold.
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