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As user preferences can be influenced by the recommendation system, it has been shown that the joint recommendation and caching policy design can significantly improve the caching networks where caching is at the BSs. However, whether and how the joint recommendation and caching policy design can provide benefits to cache-aided device-to-device (D2D) networks have not been well-understood. This paper thus contributes in this direction by modeling the offloading probability of the cache-aided D2D network and proposing a social-aware joint recommendation caching policy design. Specifically, considering the preferences and social relationship of users as well as the caching and recommendation policies of the network, we formulate an offloading probability optimization problem which is non-convex. Then, an iterative algorithm with monotonicity and convergence property is proposed to solve the problem. By simulations, we show that the proposed joint recommendation and caching policy design can significantly outperform designs that only optimize the caching policy and other reference designs. |
