Multi-Dimension Context-Based Service Recommendation Algorithm in VANET

Abstract

Aiming at the information overload and driving safety problems existing in VANET, this paper proposes a multidimension context-based service recommendation algorithm in VANET based on the recommended middleware architecture of VANET service. The middleware architecture not only shields the heterogeneity of the underlying devices, but also quickly captures the vehicle's rich real-time contextual information. The algorithm belongs to the content-based recommendation category. Firstly, the service station is filtered according to the context information, and the optional service station is selected. Secondly, the user preference model is calculated according to the user history service record. Then, the similarity between the service provided by the service station and the user preference model is calculated. Finally, the recommendation coefficient is calculated and sorted according to the recommendation coefficient, and the service that meets the personalized requirement is recommended for the user. In this paper, the Yelp real data set is used to simulate the algorithm. The simulation results show that the recommended results of the algorithm are more in line with the user's individual needs, and the accuracy of the recommendation results is improved, and the bypass probability caused by the service is reduced.