Personalized Recommendations Based on Vehicle Portraits

At present, the personalized recommendation system based on vehicle portrait has become a research hotspot. The system uses the powerful processing power of the big data platform to deeply mine the multivariate data of the vehicle. Through complex data processing and analysis technology, it constructs a comprehensive and accurate vehicle portrait, and realizes a personalized recommendation algorithm suitable for a certain scenario based on the construction of the vehicle portrait. This paper uses the vehicle-user interaction behavior, user information, and vehicle information to construct a vehicle-user bipartite graph, and constructs a vehicle similarity network based on the vehicle-user bipartite graph projection method. At the same time, according to the clustering results, the vehicle similarity network is adjusted using the similarity attenuation factor to complete the recommendation of the vehicle personalized list. The algorithm not only considers the historical behavior pattern of the user's vehicle selection and the preference characteristics of the vehicle, but also adds real-time data analysis and prediction of the vehicle, realizing accurate prediction and personalized recommendation of the user's vehicle selection. In summary, the research on the personalized recommendation system based on vehicle portrait in this paper not only provides enterprises with efficient vehicle management tools, helping them to accurately manage vehicle resources and optimize operating costs, it also provides convenient vehicle selection solutions for staff using vehicles, improves the work efficiency and travel experience of staff, and realizes the accurate docking and efficient matching of vehicle resources and user needs.