用户请求感知的边端缓存与用户推荐联合优化策略  

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

作　　者：王汝言[1,2,3] 蒋昊 唐桐 吴大鹏 钟艾玲[1,2,3] WANG Ruyan;JIANG Hao;TANG Tong;WU Dapeng;ZHONG Ailing(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China,Chongqing 400065,China;Chongqing Key Laboratory of Ubiquitous Sensing and Networking,Chongqing 400065,China)

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]先进网络与智能互联技术重庆市高校重点实验室,重庆400065 [3]泛在感知与互联重庆市重点实验室,重庆400065

出　　处：《电子与信息学报》2024年第7期2850-2859,共10页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62271096,U20A20157);重庆市自然科学基金(CSTB2023NSCQ-LZX0134);重庆市高校创新研究群体(CXQT20017);重邮信通青创团队支持计划(SCIE-QN-2022-04);重庆市教委科学技术研究项目(KJQN202300632);重庆市博士后特别资助项目(2022CQBSHTB2057);重庆市研究生科研创新项目(CYB22250)。

摘　　要：针对当前边缘缓存场景中普遍存在的用户偏好未知与高度异质问题,该文提出一种用户请求感知的边端缓存与用户推荐联合优化策略。首先,建立点击率(CTR)预测基本模型,引入对比学习方法生成高质量的特征表示,辅助因子分解机(FM)预测用户偏好;然后,基于用户偏好设计一种动态推荐机制,重塑不同用户的内容请求概率,从而影响缓存决策;最后,以用户平均内容获取时延最小化为目标建立边端缓存与用户推荐联合优化问题,将其解耦为边端缓存子问题和用户推荐子问题,分别基于区域贪婪算法和一对一交换匹配算法求解,并通过迭代更新获得收敛优化结果。仿真结果表明,相较于基准模型,引入对比学习方法的预测模型在曲线下面积(AUC)和准确率(ACC)上分别提升1.65%和1.30%,且联合优化算法能够有效降低用户平均内容获取时延,提升系统缓存性能。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.

关 键 词：边缘缓存 对比学习 推荐机制 平均时延 

分 类 号：TN929.5[电子电信—通信与信息系统]