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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2020-08-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864820301802 |
| _version_ | 1819176477386604544 |
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| author | Yaru Fu Khai Nguyen Doan Tony Q.S. Quek |
| author_facet | Yaru Fu Khai Nguyen Doan Tony Q.S. Quek |
| author_sort | Yaru Fu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T21:11:23Z |
| format | Article |
| id | doaj.art-5a5b770ba52a4f289e0e15577789c46b |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-12-22T21:11:23Z |
| publishDate | 2020-08-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-5a5b770ba52a4f289e0e15577789c46b2022-12-21T18:12:31ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482020-08-0163304311On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigmYaru Fu0Khai Nguyen Doan1Tony Q.S. Quek2The School of Science and Technology, The Open University of Hong Kong (OUHK), Kowloon, Hong Kong, ChinaInformation Systems Technology and Design, Singapore University of Technology and Design, 487372, SingaporeThe School of Science and Technology, The Open University of Hong Kong (OUHK), Kowloon, Hong Kong, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2352864820301802Artificial intelligenceContent cachingOptimization techniquesRecommendation6G |
| spellingShingle | Yaru Fu Khai Nguyen Doan Tony Q.S. Quek On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm Digital Communications and Networks Artificial intelligence Content caching Optimization techniques Recommendation 6G |
| title | On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm |
| title_full | On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm |
| title_fullStr | On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm |
| title_full_unstemmed | On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm |
| title_short | On recommendation-aware content caching for 6G: An artificial intelligence and optimization empowered paradigm |
| title_sort | on recommendation aware content caching for 6g an artificial intelligence and optimization empowered paradigm |
| topic | Artificial intelligence Content caching Optimization techniques Recommendation 6G |
| url | http://www.sciencedirect.com/science/article/pii/S2352864820301802 |
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