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

Xibin WANG  Fengji LUO  Chunyan SANG  Jun ZENG  Sachio HIROKAWA  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.2   pp.285-293
Publication Date: 2017/02/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Data Engineering, Web Information Systems
personalized recommendation,  support vector machine,  particle swarm optimization,  service computing,  

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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 information from the big data. Collaborative filtering (CF) is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. However, the conventional CF technique has some limitations, such as the low accuracy of of similarity calculation, cold start problem, etc. 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. An improved Particle Swarm Optimization (PSO) algorithm is also proposed to improve the performance of the model. The efficiency of the proposed method is verified by multiple benchmark datasets.