Personalized Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

• DOI: 10.1587/transinf.2016edp7054
• Publication Date: 2017-01-31T22:30:05Z
• Authors: Xibin WANG; Fengji LUO; Chunyan SANG; Jun ZENG; Sachio HIROKAWA
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

With the rapid development of information and Web technologies, people are facing 'information overload' in their daily lives.The personalized recommendation system (PRS) is an effective tool to assist users extract meaningful from big data.Collaborative filtering (CF) one most widely used techniques recommend products for users.However, conventional CF technique has some limitations, such as low accuracy similarity calculation, cold start problem, etc.In this paper, a PRS model based on Support Vector Machine (SVM) proposed.The proposed not only considers items' content information, but also users' demographic behavior fully capture interests preferences.An improved Particle Swarm Optimization (PSO) algorithm improve performance model.The efficiency method verified by multiple benchmark datasets.