Recommendation method for fusion of knowledge graph convolutional network

Abstract

In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph, the information of the neighboring entity is combined with the deviation. Through the integration of neighbor entity information, the local neighborhood structure can be better captured and stored in each entity, and the weight of different neighbor entities depends on the relationship between them and the specific user, which can better reflect the user's personalized interests, in order to fully demonstrate the characteristics of the entity. Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value.

A Review of Joint Applications of IoT and Deep Learning

“…Graph neural networks can also be used for recommend graph data in the Internet of Vehicles (IoV). Jiang et al (2022) [43] further explored the relationship between the two entities and other potential interests of users by constructing a graph structure and generating a knowledge graph with users and vehicles as entities. They studied recommendation techniques based on knowledge graph-KGCN using different aggregators to collect neighbourhood information.…”