An improved collaborative movie recommendation system using computational intelligence
Recommendation systems have become prevalent in recent years as they dealing with the information overload problem by suggesting users the most relevant products from a massive amount of data. For media product, online collaborative movie recommendations make attempts to assist users to access their...
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| Published in | Journal of visual languages and computing Vol. 25; no. 6; pp. 667 - 675 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.12.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1045-926X 1095-8533 |
| DOI | 10.1016/j.jvlc.2014.09.011 |
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| Abstract | Recommendation systems have become prevalent in recent years as they dealing with the information overload problem by suggesting users the most relevant products from a massive amount of data. For media product, online collaborative movie recommendations make attempts to assist users to access their preferred movies by capturing precisely similar neighbors among users or movies from their historical common ratings. However, due to the data sparsely, neighbor selecting is getting more difficult with the fast increasing of movies and users. In this paper, a hybrid model-based movie recommendation system which utilizes the improved K-means clustering coupled with genetic algorithms (GAs) to partition transformed user space is proposed. It employs principal component analysis (PCA) data reduction technique to dense the movie population space which could reduce the computation complexity in intelligent movie recom-mendation as well. The experiment results on Movielens dataset indicate that the proposed approach can provide high performance in terms of accuracy, and generate more reliable and personalized movie recommendations when compared with the existing methods.
•Proposed an optimized clustering algorithm to partition user profiles.•To represent denser profile vectors by principal component analysis transforming.•Be examined to provide high prediction accuracy and more reliable recommendations. |
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| AbstractList | Recommendation systems have become prevalent in recent years as they dealing with the information overload problem by suggesting users the most relevant products from a massive amount of data. For media product, online collaborative movie recommendations make attempts to assist users to access their preferred movies by capturing precisely similar neighbors among users or movies from their historical common ratings. However, due to the data sparsely, neighbor selecting is getting more difficult with the fast increasing of movies and users. In this paper, a hybrid model-based movie recommendation system which utilizes the improved K-means clustering coupled with genetic algorithms (GAs) to partition transformed user space is proposed. It employs principal component analysis (PCA) data reduction technique to dense the movie population space which could reduce the computation complexity in intelligent movie recom-mendation as well. The experiment results on Movielens dataset indicate that the proposed approach can provide high performance in terms of accuracy, and generate more reliable and personalized movie recommendations when compared with the existing methods.
•Proposed an optimized clustering algorithm to partition user profiles.•To represent denser profile vectors by principal component analysis transforming.•Be examined to provide high prediction accuracy and more reliable recommendations. |
| Author | Wang, Zan Wang, Zhenhua Yu, Xue Feng, Nan |
| Author_xml | – sequence: 1 givenname: Zan surname: Wang fullname: Wang, Zan email: wangzan@tju.edu.cn organization: School of Computer Software, Tianjin University, Tianjin 300072, China – sequence: 2 givenname: Xue surname: Yu fullname: Yu, Xue email: yuki@tju.edu.cn organization: College of Management and Economics, Tianjin University, Tianjin 300072, China – sequence: 3 givenname: Nan surname: Feng fullname: Feng, Nan email: fengnan@tju.edu.cn organization: College of Management and Economics, Tianjin University, Tianjin 300072, China – sequence: 4 givenname: Zhenhua surname: Wang fullname: Wang, Zhenhua email: zhw.powersystem@gmail.com organization: American Electric Power, Gahanna, OH 43230, United States |
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| Keywords | K-means Movie recommendation Collaborative filtering Sparsity data Genetic algorithms |
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| Title | An improved collaborative movie recommendation system using computational intelligence |
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