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Using Multi-objective Optimization to Solve the Long Tail Problem in Recommender System

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Advances in Knowledge Discovery and Data Mining (PAKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11441))

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Abstract

An improved algorithm for recommender system is proposed in this paper where not only accuracy but also comprehensiveness of recommendation items is considered. We use a weighted similarity measure based on non-dominated sorting genetic algorithm II (NSGA-II). The solution of optimal weight vector is transformed into the multi-objective optimization problem. Both accuracy and coverage are taken as the objective functions simultaneously. Experimental results show that the proposed algorithm improves the coverage while the accuracy is kept.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, East China Normal University, 3663 Zhong Shan Rd. N., Shanghai, China

    Jiaona Pang

  2. Computer Center, East China Normal University, 3663 Zhong Shan Rd. N., Shanghai, China

    Jun Guo & Wei Zhang

Authors
  1. Jiaona Pang
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  2. Jun Guo
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  3. Wei Zhang
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Corresponding author

Correspondence to Jun Guo .

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Pang, J., Guo, J., Zhang, W. (2019). Using Multi-objective Optimization to Solve the Long Tail Problem in Recommender System. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_24

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  • DOI: https://doi.org/10.1007/978-3-030-16142-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16141-5

  • Online ISBN: 978-3-030-16142-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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