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Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Author

Listed:
  • Qiuzhen Lin
  • Xiaozhou Wang
  • Bishan Hu
  • Lijia Ma
  • Fei Chen
  • Jianqiang Li
  • Carlos A. Coello Coello

Abstract

Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.

Suggested Citation

  • Qiuzhen Lin & Xiaozhou Wang & Bishan Hu & Lijia Ma & Fei Chen & Jianqiang Li & Carlos A. Coello Coello, 2018. "Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation," Complexity, Hindawi, vol. 2018, pages 1-18, November.
    • Handle: RePEc:hin:complx:1716352
    DOI: 10.1155/2018/1716352
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    References listed on IDEAS

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    1. Geng, Bingrui & Li, Lingling & Jiao, Licheng & Gong, Maoguo & Cai, Qing & Wu, Yue, 2015. "NNIA-RS: A multi-objective optimization based recommender system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 383-397.
    2. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    Cited by:

    1. Wu Lin & Qiuzhen Lin & Zexuan Zhu & Jianqiang Li & Jianyong Chen & Zhong Ming, 2019. "Evolutionary Search with Multiple Utopian Reference Points in Decomposition-Based Multiobjective Optimization," Complexity, Hindawi, vol. 2019, pages 1-22, April.
    2. Yuchao Su & Qiuzhen Lin & Jia Wang & Jianqiang Li & Jianyong Chen & Zhong Ming, 2019. "A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition," Complexity, Hindawi, vol. 2019, pages 1-11, May.

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