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

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.

[1]  Dean P. Foster,et al.  Clustering Methods for Collaborative Filtering , 1998, AAAI 1998.

[2]  Yi Zhang,et al.  Efficient bayesian hierarchical user modeling for recommendation system , 2007, SIGIR.

[3]  Yang Gao,et al.  Adequate is better: particle swarm optimization with limited-information , 2015, Appl. Math. Comput..

[4]  Michael J. Pazzani,et al.  Learning Collaborative Information Filters , 1998, ICML.

[5]  Qiang Wu,et al.  McRank: Learning to Rank Using Multiple Classification and Gradient Boosting , 2007, NIPS.

[6]  Tong Zhang,et al.  Recommender Systems Using Linear Classifiers , 2002 .

[7]  Z. Dong,et al.  Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations , 2014, IEEE Transactions on Power Systems.

[8]  RiedlJohn,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 2017 .

[9]  Shuyuan Yang,et al.  Global discriminative-based nonnegative spectral clustering , 2016, Pattern Recognit..

[10]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[11]  Shiguo Lian,et al.  Adaptive personalized recommendation based on adaptive learning , 2011, Neurocomputing.

[12]  Fang Liu,et al.  Coupled compressed sensing inspired sparse spatial-spectral LSSVM for hyperspectral image classification , 2015, Knowl. Based Syst..

[13]  Guntram Berti.  GrAL--the grid algorithms library , 2006, Future Gener. Comput. Syst..

[14]  Mingcang Zhu,et al.  Housing price forecasting based on genetic algorithm and support vector machine , 2011, Expert Syst. Appl..

[15]  Fang Liu,et al.  A Novel Immune Clonal Algorithm for MO Problems , 2012, IEEE Transactions on Evolutionary Computation.

[16]  Ying Chen,et al.  Improving option price forecasts with neural networks and support vector regressions , 2009, Neurocomputing.

[17]  Tao Li,et al.  Product recommendation with temporal dynamics , 2012, Expert Syst. Appl..

[18]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[19]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[20]  Yangyang Li,et al.  Self-representation based dual-graph regularized feature selection clustering , 2016, Neurocomputing.

[21]  Fei Wang,et al.  Fast semi-supervised clustering with enhanced spectral embedding , 2012, Pattern Recognit..

[22]  E. Fridell,et al.  The mechanism for NOx storage , 2000 .

[23]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[24]  Shunzhi Zhu,et al.  Personalized News Recommendation: A Review and an Experimental Investigation , 2011, Journal of Computer Science and Technology.

[25]  Yoon Ho Cho,et al.  Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations , 2010, Inf. Sci..

[26]  Zhao Yang Dong,et al.  Assessing the Transmission Expansion Cost With Distributed Generation: An Australian Case Study , 2014, IEEE Transactions on Smart Grid.

[27]  Michael J. Pazzani,et al.  Learning and Revising User Profiles: The Identification of Interesting Web Sites , 1997, Machine Learning.

[28]  Wen Ying,et al.  Heterogeneous Strategy Particle Swarm Optimization , 2016, IEEE Transactions on Circuits and Systems II: Express Briefs.