Personalized recommendation system PRS is an effective tool to automatically extract meaningful information from the big data of the users. Collaborative filtering is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. In this paper, a PRS model based on the support vector machine SVM is proposed. The proposed model not only considers the items' content information, but also the users' demographic and behavior information to fully capture the users' interests and preferences. Meanwhile, an improved particle swarm optimization PSO algorithm is applied to optimize the SVM's learning parameters. The efficiency of the proposed method is verified by multiple benchmark datasets.
['Xibin Wang', 'Junhao Wen', 'Fengji Luo', 'Wei Zhou', 'Haijun Ren']
Personalized Recommendation System Based on Support Vector Machine and Particle Swarm Optimization