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

Yaru Fu, Yue Zhang, Angus Wong, Tony Q.S. Quek

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
期刊IEEE Transactions on Mobile Computing
DOIs
出版狀態Accepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • 軟體
  • 電腦網路與通信
  • 電氣與電子工程

指紋

深入研究「Revenue Maximization: The Interplay Between Personalized Bundle Recommendation and Wireless Content Caching」主題。共同形成了獨特的指紋。

引用此