Since most existing Point-Of-Interest (POI) recommendation algorithms ignore the complexity of the modeling of the fusion of user dynamic and static preferences, a POI recommendation algorithm called CLSR (Combing Long Short Recommendation) was proposed that combined complex dynamic user preferences and general static user preferences. Firstly, in the process of modeling complex dynamic preferences, a hybrid neural network was designed based on the user's check-in behaviors and the skip behaviors in check-in behaviors to achieve the modeling of complex dynamic interests of the user. Secondly, in the process of general static preference modeling, a high-level attention network was used to learn the complex interactions between the user and POIs. Thirdly, a multi-layer neural network was used to further learn and express the above dynamic preferences and static preferences. Finally, a unified POI recommendation framework was used to integrate the preferences. Experimental results on real datasets show that, compared with FPMC-LR (Factorizing Personalized Markov Chain and Localized Region), PRME (Personalized Ranking Metric Embedding), Rank-GeoFM (Ranking based Geographical Factorization Method) and TMCA (Temporal and Multi-level Context Attention), CLSR has the performance greatly improved, and compared to the optimal TMCA among the comparison methods, the proposed algorithm has the precision, recall and normalized Discounted Cumulative Gain (nDCG) increased by 5.8%, 5.1%, and 7.2% on Foursquare dataset, and 7.3%, 10.2%, and 6.3% on Gowalla dataset. It can be seen that CLSR algorithm can effectively improve the results of POI recommendation.

In Internet of Vehicles(IoV)environment,a single vehicle cannot meet all the time-sensitive driving safety requirements because of limited capability on information acquiring and processing. Cooperation among vehicles to enhance information sharing and channel access ability is inevitable. In order to solve these problems,a cooperative feedback control algorithm based dynamic reinforcement model for driving safety was proposed. Firstly,a virtual fleet cooperation model was proposed to improve the precision and expand the range of global traffic sensing,and a stable cooperation relationship was constructed among vehicles to form cooperative virtual fleet while avoiding channel congestion. Then,a joint optimization model focusing on message transmission and driving control was implemented,and the deep fusion of heterogeneous traffic data was used to maximize the safety utility of IoV. Finally,an adaptive feedback control model was proposed according to the prediction on spatial-temporal change of traffic flow,and the driving safety strategy was able to be adjusted in real-time. Simulation results demonstrate that the proposed model can obtain good performance indexes under different traffic flow distribution models, can effectively support driving assisted control system, and reduce channel congestion while maintaining driving safety.