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Integrating Triangle and Jaccard similarities for recommendation

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  • Shuang-Bo Sun
  • Zhi-Heng Zhang
  • Xin-Ling Dong
  • Heng-Ru Zhang
  • Tong-Jun Li
  • Lin Zhang
  • Fan Min

Abstract

This paper proposes a new measure for recommendation through integrating Triangle and Jaccard similarities. The Triangle similarity considers both the length and the angle of rating vectors between them, while the Jaccard similarity considers non co-rating users. We compare the new similarity measure with eight state-of-the-art ones on four popular datasets under the leave-one-out scenario. Results show that the new measure outperforms all the counterparts in terms of the mean absolute error and the root mean square error.

Suggested Citation

  • Shuang-Bo Sun & Zhi-Heng Zhang & Xin-Ling Dong & Heng-Ru Zhang & Tong-Jun Li & Lin Zhang & Fan Min, 2017. "Integrating Triangle and Jaccard similarities for recommendation," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0183570
    DOI: 10.1371/journal.pone.0183570
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    References listed on IDEAS

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    1. Paul Resnick & Neophytos Iacovou & Mitesh Suchak & Peter Bergstrom & John Riedl, 1994. "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Working Paper Series 165, MIT Center for Coordination Science.
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    Cited by:

    1. Mubbashir Ayub & Mustansar Ali Ghazanfar & Zahid Mehmood & Tanzila Saba & Riad Alharbey & Asmaa Mahdi Munshi & Mayda Abdullateef Alrige, 2019. "Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-29, August.
    2. Junmei Feng & Xiaoyi Fengs & Ning Zhang & Jinye Peng, 2018. "An improved collaborative filtering method based on similarity," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-18, September.
    3. Fuyu Xu & Kate Beard, 2021. "A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-23, June.
    4. Gourav Jain & Tripti Mahara & S. C.Sharma, 2023. "Effective time context based collaborative filtering recommender system inspired by Gower’s coefficient," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 429-447, February.

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