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NNIA-RS: A multi-objective optimization based recommender system

Author

Listed:
  • Geng, Bingrui
  • Li, Lingling
  • Jiao, Licheng
  • Gong, Maoguo
  • Cai, Qing
  • Wu, Yue

Abstract

With the rapid development of the Internet, we have entered an era of information explosion. In this kind of situation, recommender systems appear. It is meaningful that recommender systems help people to get useful and valuable items from massive data (e.g. movies, music, books, jokes). The traditional recommender systems especially collaborative filtering make recommendations based on similarity between items or users. Under this strategy, the similarities between the items in the recommendation list are extremely high so as to guarantee the accuracy of a recommendation, but it easily results in the loss of diversity. Actually, the task of recommender systems can be modeled as a multi-objective optimization problem. By taking into account of the recommendation accuracy and diversity, a multi-objective evolutionary algorithm for recommender systems is proposed. Each run of the proposed algorithm can produce a set of nondominated solutions. Each solution denotes a unique recommendation list to a target user. Experiments demonstrate that the proposed algorithm is promising and effective in terms of recommendation diversity and novelty.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:424:y:2015:i:c:p:383-397
    DOI: 10.1016/j.physa.2015.01.007
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    References listed on IDEAS

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    Cited by:

    1. Ahmadi, Mohammad H. & Ahmadi, Mohammad Ali & Sadatsakkak, Seyed Abbas, 2015. "Thermodynamic analysis and performance optimization of irreversible Carnot refrigerator by using multi-objective evolutionary algorithms (MOEAs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1055-1070.
    2. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    3. 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.

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