Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) ignoring personalized spatial and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users and (2) insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

With the widespread application of location‐based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast‐updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree‐indexed searching method is proposed to quickly find the answers to user‐defined queries on frequently updating road networks. First, a novel index structure, called the double tree‐hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index‐enhanced search algorithm is proposed to quickly find the k‐nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state‐of‐the‐art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.

The services in the Internet of Things (IoT) are the key components to realize the value of IoT. The entity-oriented services are discovered from data. However, a large number of heterogeneous data and entities in IoT increase the difficulty of service development. For this, we propose a cyber-physical-social model to recommend services in IoT. The model consists of four layers: in the physical layer, the individual behavior pattern is defined. The system layer is responsible for handling interaction data to solve the heterogeneous data problem. The cyber layer is the agent layer, where we use the defined agents to establish service logic, shielding the entity heterogeneous problem. In the social layer, we explore the behavior similarity between individual users, achieving entity interaction in different scenes. In experiments, we obtain the data from 5 scenes, and the data is used for 6 experiments. In terms of accuracy and response time, our model has outstanding advantages compared with the previous methods.