A Personalized Preference Learning Framework for Caching in Mobile Networks

Adeel Malik, Joongheon Kim, Kwang Soon Kim, Won Yong Shin

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: The personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters.

Original languageEnglish
Article number9007521
Pages (from-to)2124-2139
Number of pages16
JournalIEEE Transactions on Mobile Computing
Volume20
Issue number6
DOIs
Publication statusPublished - 2021 Jun 1

Bibliographical note

Funding Information:
This work was supported by the European Research Council under the EU Horizon 2020 research and innovation program / ERC grant agreement no. 725929 (project DUALITY) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.2019R1A2C2007982).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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