IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0183570.html
   My bibliography  Save this article

Integrating Triangle and Jaccard similarities for recommendation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183570
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183570&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0183570?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Charles M.C. & Ma, Paul & Wang, Charles C.Y., 2015. "Search-based peer firms: Aggregating investor perceptions through internet co-searches," Journal of Financial Economics, Elsevier, vol. 116(2), pages 410-431.
    2. Zhang, Peng & Song, Xiaoyu & Xue, Leyang & Gu, Ke, 2019. "A new recommender algorithm on signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 317-321.
    3. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    4. Nie, Da-Cheng & An, Ya-Hui & Dong, Qiang & Fu, Yan & Zhou, Tao, 2015. "Information filtering via balanced diffusion on bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 44-53.
    5. Sohn, Jeong Woong & Kim, Jin Ki, 2020. "Factors that influence purchase intentions in social commerce," Technology in Society, Elsevier, vol. 63(C).
    6. Zhang, Yi & Robinson, Douglas K.R. & Porter, Alan L. & Zhu, Donghua & Zhang, Guangquan & Lu, Jie, 2016. "Technology roadmapping for competitive technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 175-186.
    7. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    8. Zhang, Peng & Wang, Duo & Xiao, Jinghua, 2017. "Improving the recommender algorithms with the detected communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 147-153.
    9. Liebig, Jessica & Rao, Asha, 2016. "Predicting item popularity: Analysing local clustering behaviour of users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 523-531.
    10. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    11. Chen, Jianrui & Wei, Lidan & Uliji, & Zhang, Li, 2018. "Dynamic evolutionary clustering approach based on time weight and latent attributes for collaborative filtering recommendation," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 8-18.
    12. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    13. Hausmann, Ricardo & Stock, Daniel P. & Yıldırım, Muhammed A., 2022. "Implied comparative advantage," Research Policy, Elsevier, vol. 51(8).
    14. Hausmann, Ricardo & Hidalgo, Cesar A. & Stock, Daniel P. & Yildirim, Muhammed A., 2014. "Implied Comparative Advantage," Working Paper Series rwp14-003, Harvard University, John F. Kennedy School of Government.
    15. Hael Al-bashiri & Mansoor Abdullateef Abdulgabber & Awanis Romli & Hasan Kahtan, 2018. "An improved memory-based collaborative filtering method based on the TOPSIS technique," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-26, October.
    16. Ai, Jun & Cai, Yifang & Su, Zhan & Zhang, Kuan & Peng, Dunlu & Chen, Qingkui, 2022. "Predicting user-item links in recommender systems based on similarity-network resource allocation," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    17. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.
    18. Urban, Glen L. & Sultan, Fareena. & Qualls, William J. 1953-, 1998. "Trust based marketing on the internet," Working papers WP 4035-98., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    19. Su, Zhan & Zheng, Xiliang & Ai, Jun & Shen, Yuming & Zhang, Xuanxiong, 2020. "Link prediction in recommender systems based on vector similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    20. Latha, R., 2022. "Enhancing recommendation competence in nearest neighbour models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0183570. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.