Revenue Maximization: The Interplay Between Personalized Bundle Recommendation and Wireless Content Caching

In this paper, we explore the interplay between personalized bundle recommendation and cache decision on the performance of wireless edge caching networks. A revenue maximization perspective is provided. To this end, we first examine the quantitative impact of bundle recommendation on the content request probability of different users. We then specify the definition of system revenue, showing its dependence on bundle recommendation and caching policies. With that, a joint bundling, caching and recommendation decision problem is formulated to maximize the achievable system revenue, taking into account the constraints of user-distinguished recommendation quality, recommendation amount, and the cache capacity budget. To solve this non-tractable optimization problem, a divide-then-conquer methodology is adopted. Specifically, we first determine the bundle state per user, on which basis we perform the joint bundle recommendation and caching decision-making, wherein several bundling strategies with different time-complexity are devised. Last but not least, we provide detailed properties analysis for our proposed bundling and joint optimization algorithms. Comprehensive numerical simulations validate the performance enhancement of the designed solutions compared to extensive conventional single-item recommendation oriented benchmarks.

Automated summary

In this paper, the interplay between personalized bundle recommendation and cache decision on the performance of wireless edge caching networks is explored from a revenue maximization perspective. The quantitative impact of bundle recommendation on different users' content request probability is examined, and the dependence of system revenue on bundle recommendation and caching policies is specified. A joint bundling, caching, and recommendation decision problem is formulated to maximize the achievable system revenue, considering the constraints of user-distinguished recommendation quality, recommendation amount, and cache capacity budget. A divide-then-conquer methodology is adopted to solve this non-tractable optimization problem, and detailed properties analysis for the proposed bundling and joint optimization algorithms is provided. Comprehensive numerical simulations validate the performance enhancement of the designed solutions compared to extensive conventional single-item recommendation oriented benchmarks.