## 2 Related Work
The work [16] proposed a Markov model to handle recommendation-driven user requests and developed an iterative ADMM-based algorithm to minimize the access cost and maintain the quality of the recommendations to address the problem of large fluctuations in user preferences and the complex sequential consumption patterns that make traditional caching methodsineffective in edge environments. The work [17] proposed \"soft cache hits\" to improve the cache hit rate by caching not only the content that is directly requested by the user, but also content that is related to the requested content to improve the cache hit rate. It developed a model to evaluate the impact of recommended relevant content on cache performance and then proposed an optimization algorithm that analyzes the user's content access patterns and relevance, enabling dynamically adjusting the caching policy to maximize \"soft cache hits\" and reduce content access latency. The work [18] defined a streaming experience metric that captures the fundamental tradeoff between caching and recommendations and proposed an approximation algorithm under this metric that balances cache capacity constraints and recommendation quality to maximize user satisfaction and cache hit rate. The algorithm combines cache management and recommendation systems to reduce content access latency through an optimization policy. It also provided the approximation performance of the algorithm and experimental verification of its effectiveness in real applications, showing better cache utilization and recommendation results under the given tradeoff metric between caching and recommendations. Closely related to the above work, [PERSON] et al. [19] studied the problem of cooperative caching of vehicles to reduce energy consumption and delay in vehicular networking. The authors solve the problem based on a deep reinforcement learning approach by considering the constraints of tolerable latency of the task and dwell time of the vehicle. [PERSON] et al. [20] studied the joint optimization cache recommendation problem by giving a reinforcement learning-based algorithm that contains prediction and allocation phases. The prediction phase estimates the values of cache choices by considering value policy dependencies, while the allocation phase determines the cache choices for each request while satisfying global budget constraints. [PERSON] et al. [21] studied optimization in large-scale caching and recommender systems and proposed a cache-aware reinforcement learning approach to optimize recommendations through real-time computation and caching. [PERSON] et al. [22] studied the wireless edge offload scenario of device-to-device offloading, which actually recommends a user's cached content to encountered users. The strategy takes into account both personalized preferences and relative location without burdening the cellular link. [PERSON] et al. [23] studied the problem of maximizing the cache hit rate in wireless edge offload scenarios. The authors give a time-efficient iterative algorithm for solving the problem, taking into account the quality of personalized recommendations, the number of recommendations, and the cache capacity requirements for each user. [PERSON] et al. [24] investigated the use of proactive content pushing during low peak hours to improve spectrum efficiency, revenue prediction based on reinforcement learning, and solving for joint pushing, pricing, and recommending in cache-enabled wireless access networks based on linear programming. [PERSON] et al. [25] optimized content encoding by joint caching and recommendation, studied the problem of maximizing the hit rate of wireless edge caching networks, and achieved the optimal solution to this problem from a game-theoretic perspective. [PERSON] et al. [26] studied the relationship between the cost and revenue of CDNs that are reduced by recommendation, and the authors proposed a model to find the tradeoff between the two and gave an economic mechanism based on Nash bargaining to enhance the final financial returns. [PERSON] et al.