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A Personalized Preference Learning Framework for Caching in Mobile Networks

Authors
Malik, AdeelKim, JoongheonKim, Kwang SoonShin, Won-Yong
Issue Date
1-6월-2021
Publisher
IEEE COMPUTER SOC
Keywords
Device-to-device communication; Mobile computing; Libraries; Greedy algorithms; Wireless communication; Recommender systems; Computational modeling; Caching; collaborative filtering; learning; mobile network; personalized file preferences
Citation
IEEE TRANSACTIONS ON MOBILE COMPUTING, v.20, no.6, pp.2124 - 2139
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume
20
Number
6
Start Page
2124
End Page
2139
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/127881
DOI
10.1109/TMC.2020.2975786
ISSN
1536-1233
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.
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Kim, Joong heon
공과대학 (전기전자공학부)
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