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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Published in:SSCI : 2017 IEEE Symposium Series on Computational Intelligence : November 27, 2017-December 1, 2017 pp. 1 - 6
Main Authors: Wang, Pan, Zuo, Xingquan, Guo, Congcong, Li, Ruihong, Zhao, Xinchao, Luo, Chaomin
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2017
Subjects:
hybrid algorithm
Linear programming
Matrix decomposition
Measurement
multiobjective optimization algorithms
recommendation algorithms
Sociology
Sparse matrices
Statistics
Telecommunications
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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.
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
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  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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