## 2 New Technologies and Applications  
Recently, substantial data are increasing, not only through media but also through SNS. Filtering to find the desired content among this information is becoming difficult; given this, recommendation systems are being developed to solve these problems. Accordingly, one paper [2] in this Special Issue proposes a ranking and reinforcement sequence movie recommendation system using a deep learning combination model to solve data scalability, data shortage, and cold-start problems in existing recommendation systems. The authors propose a recommendation system model that analyzes new users using user information (age, gender, occupation) for recommendations and connects them with other users with similar preferences. To achieve this, they proceeded with two main processes. The first process was to apply deep learning and key converter functions to predict the next movie based on the user's sequence and information. The second process trained the transformer model and then integrated the output prediction ratings with K-means clustering to generate a Top-N recommendation system for the target user. An evaluation of the proposed system on two MovieLens datasets (100 K and 1 M) resulted in RMSE, MAE, precision, recall, and F1 score (1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 1 M dataset, respectively). A remarkable improvement was observed for cases 0.9927 and 0.8007. With the rapid development of new technologies and 5G technology, the number of vehicles continues to rapidly increase. This exponential growth, driven primarily by the emergence of Internet of Vehicles (IoV) technologies, has remarkably improved user driving experience. This improvement will lead drivers and passengers to use in-vehicle infotainment, streaming services, and real-time applications to enhance their travel experience. However, as the number of vehicles increases, computational tasks increase, content requests are duplicated, and resources are wasted. In other words, efficient compute offloading and content caching strategies are critical for the IoV to optimize performance in terms of time delay and energy consumption. Another paper [3] proposes a task offloading and content caching (TOCC) strategy based on a multi objective evolutionary algorithm, which is, in turn, based on decomposition (MOEA/D), a collaborative task offloading and content caching optimization method based on traffic flow prediction. It uses a forecasting open-source tool to extract temporal/spatial correlations to predict traffic flows to obtain a number of tasks. These are also obtained by decomposing them into several single problems using MOEA/D for task offloading and content caching. The experimental results of the proposed method verify the effectiveness of the TOCC strategy. Compared with other methods, latency is up to 29% longer, and energy consumption is up to 83% higher. Recently, the rapid development of digital technology has brought about many changes in our daily lives, and one of the most notable paradigm shifts is extended reality (XR) technology, which encompasses augmented reality (AR), virtual reality (VR), and mixed reality (MR). These technologies require highly computational tasks in high-performance 3D rendering and extensive 3D point cloud processing. These tasks are particularly computationally intensive and must run in real time and be driven by user interaction, rendering virtual environments seamlessly and without interruption. Accordingly, the transition to the cloud is essential in order to provide users with a superior experience by building an XR environment in a more modern and scalable manner. However, edge/fog computing requires research focused on network bandwidth optimization and real-time processing optimization. Therefore, another paper [4] proposes a new XR system that utilizes edge computing and fog computing to overcome the limitations of the existing cloud-based XR environment.