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

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

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)4253-4265
Number of pages13
JournalIEEE Transactions on Mobile Computing
Volume22
Issue number7
DOIs
Publication statusPublished - 1 Jul 2023

Keywords

  • Bundle recommendation
  • caching placement
  • recommendation decision
  • revenue maximization

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