A multiobjective genetic algorithm based hybrid recommendation approach
Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two...
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| Vydáno v: | SSCI : 2017 IEEE Symposium Series on Computational Intelligence : November 27, 2017-December 1, 2017 s. 1 - 6 |
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IEEE
01.11.2017
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| Abstract | Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches. |
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| AbstractList | Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates an item-based collaborative filtering, a user-based collaborative filtering and a matrix factorization method. The approach considers the two objectives of recommendation's accuracy and diversity simultaneously. First, a set of items is created separately by each of the three methods. Then, items produced by the three methods are combined into a set of candidate items. Finally, a multiobjective genetic algorithm is adopted to choose a set of Pareto recommendation lists from the set. Experimental results show that the proposed approach is very effective and is able to produce better Pareto solutions than those comparative approaches. |
| Author | Zuo, Xingquan Guo, Congcong Zhao, Xinchao Luo, Chaomin Wang, Pan Li, Ruihong |
| Author_xml | – sequence: 1 givenname: Pan surname: Wang fullname: Wang, Pan organization: School of Computer Science, Beijing, University of Posts and Telecommunications, Beijng, China – sequence: 2 givenname: Xingquan surname: Zuo fullname: Zuo, Xingquan organization: School of Computer Science, Beijing, University of Posts and Telecommunications, Beijng, China – sequence: 3 givenname: Congcong surname: Guo fullname: Guo, Congcong organization: School of Computer Science, Beijing, University of Posts and Telecommunications, Beijng, China – sequence: 4 givenname: Ruihong surname: Li fullname: Li, Ruihong organization: School of Computer Science, Beijing, University of Posts and Telecommunications, Beijng, China – sequence: 5 givenname: Xinchao surname: Zhao fullname: Zhao, Xinchao organization: School of Science, Beijing University of Posts and Telecommunications, Beijng, China – sequence: 6 givenname: Chaomin surname: Luo fullname: Luo, Chaomin organization: Department of Electrical and Computer Engineering, University of Detroit Mercy, MI, USA |
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| Snippet | Personalized recommendation approaches have received much attention over the years. In this paper, we propose a hybrid recommendation approach that integrates... |
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| SubjectTerms | hybrid algorithm Linear programming Matrix decomposition Measurement multiobjective optimization algorithms recommendation algorithms Sociology Sparse matrices Statistics Telecommunications |
| Title | A multiobjective genetic algorithm based hybrid recommendation approach |
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