title: A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization.
abstract: With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.
authors.affiliation: Chongqing University of Posts and Telecommunications, School of Software Engineering, Chongqing, China.|Chongqing Engineering Research Center of Software Quality Assurance, Testing and Assessment, Chongqing, China.
authors.forename: Xibin
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Civil Engineering, The University of Sydney, NSW, Sydney, Australia.|State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China.
authors.forename: Fengji
authors.initials: F
authors.lastname: Luo
authors.affiliation: Chongqing University of Posts and Telecommunications, School of Software Engineering, Chongqing, China.|Chongqing Engineering Research Center of Software Quality Assurance, Testing and Assessment, Chongqing, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Qian
authors.affiliation: School of Civil Engineering, The University of Sydney, NSW, Sydney, Australia.
authors.forename: Gianluca
authors.initials: G
authors.lastname: Ranzi
mesh_terms: D006801:Humans; D009040:Motion Pictures; D060388:Support Vector Machine