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.
维普
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