Intelligent recommendation algorithm for social networks based on gradient particle swarm optimization

Abstract

Aiming to improve the intelligent recommendation ability of users in social networks, an intelligent recommendation algorithm for social networks based on gradient particle swarm optimization (GPSO) is proposed. Under the limited sample training, the user association relationship model of social network is constructed, and the hybrid kernel function and the global kernel function are constructed to extract the correlation amount feature of the social network recommendation information. The hybrid particle swarm optimization algorithm is used for adaptive learning of social network recommendation, Logistic chaotic mapping is used to control the convergence of recommendation process, and the potential characteristics of network users and the universality and ergodicity of social features are analyzed. The particle swarm optimization and adaptive optimization of social network recommendation are implemented by using gradient algorithm to realize swarm intelligence recommendation of social network user information. The simulation results show that the proposed algorithm is more accurate and convergent, and it avoids falling into the local optimal solution in the process of intelligent recommendation of social network. The intelligence and global stability of social network recommendation are improved.