Personalized news recommendations focus on providing users with news that fits their interests and alleviates their information overload. User preference modeling is crucial for achieving personalized news recommendations, and user preferences are usually expressed as long-term and short-term user preferences. Existing news recommendation methods have difficulty accurately matching user preferences with candidate news, which is the main challenge plaguing the news recommendation field. For this purpose, we propose a novel news recommendation method based on candidate-aware long- and short-term preference modeling, named NRCLS. The method contains: (1) a Candidate-Enhanced graph attention network, which learns high-order structural information in graphs, mines the potential relationship between long-term user preferences and candidate news; and (2) a candidate-aware attention network that incorporates news subtopics. In this network, the dynamic impact of candidate news on short-term user preferences is considered. Results on real-world datasets demonstrate the effectiveness of our proposed method in improving the performance of news recommendations. We have also conducted several ablation studies that demonstrate the effectiveness of the core module in NRCLS.

Three-dimensional (3D) path planning is a crucial technology for ensuring the efficient and safe flight of UAVs in complex environments. Traditional path planning algorithms often find it challenging to navigate complex obstacle environments, making it challenging to quickly identify the optimal path. To address these challenges, this paper introduces a Nutcracker Optimizer integrated with Hyperbolic Sine–Cosine (ISCHNOA). First, the exploitation process of the sinh cosh optimizer is incorporated into the foraging strategy to enhance the efficiency of nutcracker in locating high-quality food sources within the search area. Secondly, a nonlinear function is designed to improve the algorithm’s convergence speed. Finally, a sinh cosh optimizer that incorporates historical positions and dynamic factors is introduced to enhance the influence of the optimal position on the search process, thereby improving the accuracy of the nutcracker in retrieving stored food. In this paper, the performance of the ISCHNOA algorithm is tested using 14 classical benchmark test functions as well as the CEC2014 and CEC2020 suites and applied to UAV path planning models. The experimental results demonstrate that the ISCHNOA algorithm outperforms the other algorithms across the three test suites, with the total cost of the planned UAV paths being lower.

As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial clustering center of mass. In this paper, an improved honey badger optimization algorithm is proposed: (1) The population is initialized using sin chaos to make the population uniformly distributed. (2) The density factor is improved to enhance the optimization accuracy of the population. (3) A nonlinear inertia weight factor is introduced to prevent honey badger individuals from relying on the behavior of past individuals during position updating. (4) To improve the diversity of solutions, random opposition learning is performed on the optimal individuals. The improved algorithm outperforms the comparison algorithm in terms of performance through experiments on 23 benchmark test functions. Finally, in this paper, the improved algorithm is applied to K-means clustering and experiments are conducted on three data sets from the UCI data set. The results show that the improved honey badger optimized K-means algorithm improves the clustering effect over the traditional K-means algorithm.