A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation

WANG Ruyan; JIANG Hao; TANG Tong; WU Dapeng; ZHONG Ailing

doi:10.11999/JEIT230898

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WANG Ruyan, JIANG Hao, TANG Tong, WU Dapeng, ZHONG Ailing. A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230898

Citation:

WANG Ruyan, JIANG Hao, TANG Tong, WU Dapeng, ZHONG Ailing. A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230898

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A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation

doi: 10.11999/JEIT230898

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62271096, U20A20157), The Natural Science Foundation of Chongqing, China (CSTB2023NSCQ-LZX0134), The University Innovation Research Group of Chongqing (CXQT20017), The Youth Innovation Group Support Program of ICE Discipline of CQUPT (SCIE-QN-2022-04), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN202300632), The Chongqing Postdoctoral Special Funding Project (2022CQBSHTB2057), Chongqing Postgraduate Research and Innovation Project (CYB22250)

Received Date: 2023-08-15
Rev Recd Date: 2024-01-17

Available Online: 2024-01-23

Abstract

Abstract

Considering the problem of unknown and highly heterogeneous user preference in the current edge caching scenario, a joint optimization strategy of user request perceived edge caching and user recommendation is proposed. Firstly, the basic model of Click Through Rate (CTR) prediction is established, and the contrastive learning method is introduced to generate high-quality feature representation, which could better help Factorization Machine(FM) model to predict user preference. Then, based on the predicted user preference, a dynamic recommendation mechanism is designed to reshape the content request probability of different users, thereby affecting cache decision; Finally, a joint optimization problem of edge caching and user recommendation is established with the goal of minimizing the average user content acquisition delay. It is decoupled into edge caching subproblem and user recommendation subproblem, and solved based on the region greedy algorithm and one-to-one exchange matching algorithm, respectively. The convergence optimization results are obtained through iterative update. The results show that compared with the benchmark model, the contrastive learning method has improved Area Under Curve (AUC) and ACCuracy (ACC) by 1.65% and 1.30%, respectively, and the joint optimization algorithm can effectively reduce the average content acquisition latency of users and improve the system cache performance.
- Edge caching,
- Contrastive learning,
- Recommendation mechanism,
- Average delay

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References(20)

References

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