Personalized Information Recommendation Based on MPF Users Clustering in Network Virtual Environments

As the uneasy sense of three-dimensional space and non-intuitiveness of resources information, it is difficult for users to find interesting or valuable information in network virtual environments. Therefore, a new personalized information recommendation algorithm based on MPF users clustering would be proposed in this paper. The MPF, namely Fuzzy C-Means (FCM) clustering based on Multi-Objects Particle Swarm Optimization (MOPSO), combines the respective advantages of PSO and FCM, which could prevent from those defects, for FCM to be susceptible to initial value and noisy data, and PSO to be easily falling into local optimum and so on. In order to improve clustering effect, we design the particle fitness function based on dual-objectives (intra-class distance and inter-class distance) in PSO. Finally, the standard data set and simulation data set are applied to test the personalized information recommendation algorithm based on MPF users clustering. The experimental result shows that this algorithm was of good performances.