Personalized news recommendation is an important technology to help users obtain news information they are interested in and alleviate information overload. In recent years, news recommendation has been increasingly widely studied and has achieved remarkable success in improving the news reading experience of users. In this paper, we provide a comprehensive overview of personalized news recommendation approaches. Firstly, we introduce personalized news recommendation systems according to different needs and analyze the characteristics. And then, a three-part research framework on personalized news recommendation is put forward. Based on the framework, the knowledge and methods involved in each part are analyzed in detail, including news datasets and processing techniques, prediction models, news ranking and display. On this basis, we focus on news recommendation methods based on different types of graph structure learning, including user–news interaction graph, knowledge graph and social relationship graph. Lastly, the challenges of the current news recommendation are analyzed and the prospect of the future research direction is presented.

With the exhaustion of oil resources and the aggravation of environmental pollution, electric vehicles, as the main force of new energy consumption, have a more and more promising development prospect. In China, the utilization rate of charging facilities in the public is very low, and there is a large number of redundant charging stations that waste resources. Charging station congestion, meanwhile, is one of the reasons why it is difficult to charge for electric vehicles. This paper proposes a data-driven approach to optimize the existing charging station network by eliminating redundant charging stations, and to identify the charging station congestion areas in the original charging network to provide suggestions for further solving the difficulty of charging electric vehicles. Firstly, we infer that the fine-grained charging situation (consisting of the waiting time and the visiting rate) at different stations. Using a 3D tensor, we model the charging behavior of the electric vehicle, in which the three dimensions represent stations, hours, and days respectively. Secondly, for times and stations with sparse data, we use a context-aware tensor collaborative decomposition method to estimate the situation. For charging stations in a specific period of time, we separately set up a queue system for them to estimate their visiting rate and detect the distribution characteristics of EV charging hotspots in the city. Finally, we introduce a flexible scoring function to evaluate the usage benefits of charging stations and propose a heuristic network expansion algorithm to optimize the network. Applying the data-driven approach to Wuhan city, the results show that using our method can eliminate redundant sites while increasing utilization and find charging station congestion area to guide the government to further charging station planning. Our approach can be adapted for other optimal problems such as chain supermarket layout, public facility planning, and resources configuration using trajectory data.

High-precision three-dimensional road networks in open-pit mines play a crucial role in production planning, truck dispatching, and unmanned driving. Compared to urban road networks, the boundaries of open-pit mine roads are indistinct, with varying widths. The colors of these roads blend with the surrounding environments and they undergo rapid changes. Thus, accurately, efficiently, and timely obtaining mining road networks still faces many challenges. With the development and popularization of UAV technology, it is now possible to obtain real-time spatial data. We propose a HDMSCA (hybrid dilated multi-scale convolution attention) unit and design the D-SegNeXt network. This method offers several advantages. First, it reduces computational complexity and enlarges the receptive field through hybrid dilated convolution. Second, residual networks and multi-scale convolutions can extract local, distant, long, and narrow features, thereby enhancing the network’s ability to capture long-range dependencies. Additionally, we construct an OPM (open-pit mine road) dataset and test the models on it. The experimental results demonstrate that our model outperforms several benchmark networks in both image classification and road extraction. Our D-SegNeXt model achieves a Top-1 acc score of 82.8% on ImageNet-1k, an IoU score of 75.59% on the open-pit mine road dataset and an IoU score of 67.96% on the DeepGlobe Road Extraction Challenge dataset. Our dataset and code are available at https://github.com/orgs/D-SegNeXt/repositories.

User-POI rating matrix is one of the current research hotspot of POI recommendation algorithms, the goal of which is to obtain the POIs with the highest user satisfaction. However, most existing POI recommendation algorithms ignore the diversity of recommended POI list, and the POIs in the recommended list are usually similar to each other, which cannot effectively broad the user’s perspectives. To solve this problem, a diverse and personalized recommendation method EBPRMF (Embedded Bayesian Personalized Ranking Matrix Factorization) that integrates the embedded features of the geographic-social relationships of POIs is proposed. First, by embedding and compressing the geographic and social relationships between POIs, a geographic-social relationship embedding model is constructed to evaluate the coupled correlation between POIs. The correlation between all POIs forms a POI correlation matrix. And then the spectral clustering method is leveraged to cluster the POIs according to the correlation matrix, and several different POI clusters can be obtained. Next, the Bayesian personalized ranking matrix factorization model is proposed to select POI that is close to the user’s preference from each cluster, and finally a recommendation list of POIs that is both diverse and personalized is obtained. Experimental results demonstrate that the geographic-social relationship embedding model can well represent the location and social relationship between POIs, and has a good clustering effect. The diversity of recommendation list is high, and the sorted list can be well satisfied with user preferences.