On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching (RCC) at the edge enables a significant reduction of the network latency and the backhaul load, thereby invigorating ubiquitous latency-sensitive innovative services. However, the effectiveness of RCC strategies is highly dependent on explicit information as rega...

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Bibliographic Details
Published inDigital communications and networks Vol. 6; no. 3; pp. 304 - 311
Main Authors Fu, Yaru, Doan, Khai Nguyen, Quek, Tony Q.S.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.08.2020
The School of Science and Technology, The Open University of Hong Kong (OUHK), Kowloon, Hong Kong, China%Information Systems Technology and Design, Singapore University of Technology and Design, 487372, Singapore
KeAi Communications Co., Ltd
Subjects
Artificial intelligence
Content caching
Optimization techniques
Recommendation
Optimization techniques
6G
Artificial intelligence
Content caching
Recommendation
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Summary:Recommendation-aware Content Caching (RCC) at the edge enables a significant reduction of the network latency and the backhaul load, thereby invigorating ubiquitous latency-sensitive innovative services. However, the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’ content request patterns, the sophisticated caching placement policy, and the personalized recommendation tactics. In this article, we investigate how the potentials of Artificial Intelligence (AI) and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era. Towards this end, we first elaborate on the hierarchical RCC network architecture. Then, the devised AI and optimization empowered paradigm is introduced, whereas AI and optimization techniques are leveraged to predict the users’ content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision, respectively. Through extensive case studies, we validate the effectiveness of AI-based predictors in estimating users’ content preference and the superiority of optimized RCC policies over the conventional benchmarks. At last, we shed light on the opportunities and challenges in the future.
ISSN:2352-8648
2352-8648
DOI:10.1016/j.dcan.2020.06.005