A Joint Optimization Strategy for User Request Perceived Edge Caching and User Recommendation
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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。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.
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Key words:
- Edge caching /
- Contrastive learning /
- Recommendation mechanism /
- Average delay
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表 1 不同模型引入对比学习方法后性能对比
模型 AUC ACC Loss FM 0.8064 0.7375 0.5264 FM_CL 0.8197 0.7471 0.5088 DeepFM 0.8117 0.7401 0.5203 DeepFM_CL 0.8126 0.7411 0.5196 xDeepFM 0.8123 0.7402 0.5200 xDeepFM_CL 0.8134 0.7414 0.5190 DCN_V2 0.8154 0.7432 0.5155 DCN_V2_CL 0.8171 0.7448 0.5135 
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表 2 仿真参数表
参数名 参数值 参数名 参数值 MBS覆盖范围 300 m D2D带宽 20 MHz SBS覆盖范围 150 m 无线回程带宽 20 MHz UE间通信阈值 60 m MBS发送功率 46 dBm 文件库大小 100 SBS发送功率 30 dBm 内容大小 10 Mbit UE发送功率 23 dBm SBS缓存容量 400 Mbit 噪声功率谱密度 174 dBm/Hz MBS带宽 10 MHz 路径损耗因子 4 SBS带宽 20 MHz 系统参数 0.01
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