## 2 Related Work  
As a classical topic in data networks, the main challenge for caching is how to accurately predict user demand and content popularity. Because content demand is temporally localized (i.e., content relevance is time-dependent, and similar content is frequently accessed in a short period of time), demand prediction for user content should take into account the temporal localization between contents. If the user content demand is accurately predicted, then the cache hit rate can be improved, which is the main problem to be solved by CDNs. CDNs are deployed by network operators, and their customers are mainly content providers such as YouTube, TikTok, etc., in recent years. One of the core competencies of these content providers is the recommendation of content, which can enhance user stickiness and thus improve the revenue of content providers. Therefore, the joint optimization of caching and recommender systems in CDNs has become an important research area, which is also the core topic of our paper. To the best of our knowledge, the joint role of caching and recommendation is considered simultaneously in [8, 9, 10, 11, 12, 13, 14]. The first four of them address caching recommendations for video content, while the rest address caching recommendations for generic content. The work [8] seems to be the first that makes caching decisions based on recommender system decisions. Their recommendation-driven, caching-based scheme outperforms traditional content prediction schemes, which, however, disappear in real-world scenarios where caching serves a larger number of users. The work [10] proposed to cache the most popular content locally rather than globally. The work [12] proposed a method to identify cached content for CDNs based on a recommender system, but it was not quantitatively evaluated. What refs. [8, 10, 12] have in common is that caching is based on the recommender system. In other words, they consider the post-recommendation user preferences as the actual user preferences. Our work differs from theirs in that our recommender system is not used purely for the prediction of user content demand but as a user preference moderation tool for balancing user preferences with performance metrics. Our proposed algorithm tunes user preferences during the course of their prediction to improve network performance. In fact, the studies in [9, 11, 13, 14] are closer to ours. The work [9] ranked cached videos higher by rearranging the list of related videos, thus influencing users' preference and then boosting the cache hit rate. The work [13] studied peer-to-peer (P2P) systems, and its algorithm mainly solves the recommendation problem considering the cost of content distribution. The work [14] proposed a framework for proactive resource and demand allocation and theoretically analyzed that the delivery cost of proactive caching will be lower. Their recommender system enhances user demand certainty by actively modifying user ratings of content. The work [11] proposed the concept of \"soft cache hit\", i.e., if a user clicks on a relevant alternative to unhit content (which is already cached), it will be considered a \"soft cache hit\", and also studied the caching-recommendation joint optimization problem. In addition, the work [15] proposed a method to jointly optimize the content caching and recommendation system, aiming to improve the cache hit rate and user experience in regional networks. By analyzing the user's request patterns and behaviors and dynamically adjusting the cached and recommended contents, it achieves more efficient content distribution and recommendation. However, its deployment is difficult due to high computational complexity and the need for frequent policy changes.