With the widespread use of mobile devices and emerging mobile applications, the exponential growth of traffic in mobile networks has caused problems such as network congestion, large delay, and poor user experience that cannot satisfy the needs of mobile users. Edge caching technology can greatly relieve the transmission pressure of wireless networks through the reuse of hot contents in the network. At the same time, it has become one of the key technologies in 5G/Beyond 5G Mobile Edge Computing (MEC) to reduce the network delay of user requests and thus improve the network experience of users. Focusing on mobile edge caching technology, firstly, the application scenarios, main characteristics, execution process, and evaluation indicators of mobile edge caching were introduced. Secondly, the edge caching strategies with energy efficiency, delay, hit ratio, and revenue maximization as optimization goals were analyzed and compared, and their key research points were summarized. Thirdly, the deployment of the MEC servers supporting 5G was described, based on this, the green mobility-aware caching strategy in 5G network and the caching strategy in 5G heterogeneous cellular network were analyzed. Finally, the research challenges and future development directions of edge caching strategies were discussed from the aspects of security, mobility-aware caching, edge caching based on reinforcement learning and federated learning and edge caching for Beyond 5G/6G networks.
In recommendation systems, recommendation results are affected by the matter that rating data is characterized by large volume, high dimensionality, extreme sparsity, and the limitation of traditional similarity measuring methods in finding the nearest neighbors, including huge calculation and inaccurate results. Aiming at the poor recommendation quality, this paper presented a new collaborative filtering recommendation algorithm based on Exact Euclidean Locality-Sensitive Hashing (E2LSH). Firstly, E2LSH algorithm was utilized to lower dimensionality and construct index for large rating data. Based on the index, the nearest neighbor users of target user could be obtained with great efficiency. Then, a weighted strategy was applied to predict the user ratings to perform collaborative filtering recommendation. The experimental results on typical dataset show that the proposed method can overcome the bottleneck of high dimensionality and sparsity to some degree, with high running efficiency and good recommendation performance.