Graph Neural Network Recommendation Algorithm Integrating Multi-Head Attention Mechanism

Recommendation system has become an important way to obtain information from massive data. Compared with active retrieval, the recommendation system based on deep learning has stronger predictability and can provide users with recommendation results with higher reference value. GNN is specifically designed for graph-structured data to extract features from graph-structured data effectively and efficiently. Incorporating attention mechanisms into CNNS improves the model's ability to understand and capture complex dependencies, thereby solving the problem of information overload. Based on the previous research results, a mathematical model is constructed and a simulation experiment is carried out. The mathematical model includes neural cooperative over-worry algorithm, multi-head attention mechanism algorithm and CUD accelerated computation. In the simulation experiment, a baseline model was selected and run on Movielens 10 data set respectively with the model constructed in this paper, which proves the superiority of the model constructed in this paper. The research results of this paper have improved the scientific method of building recommendation systems, and are of great significance for the construction of business models in the era of big data.