IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i1d10.1007_s13198-022-01813-z.html
   My bibliography  Save this article

Effective time context based collaborative filtering recommender system inspired by Gower’s coefficient

Author

Listed:
  • Gourav Jain

    (Indian Institute of Technology Roorkee)

  • Tripti Mahara

    (Christ University)

  • S. C.Sharma

    (Indian Institute of Technology Roorkee)

Abstract

The fast growth of Internet technology in recent times has led to a surge in the number of users and amount of information generated. This substantially contributes to the popularity of recommendation systems (RS), which provides personalized recommendations to users based on their interests. A RS assists the user in the decision-making process by suggesting a suitable product from various alternatives. The collaborative filtering (CF) technique of RS is the most prevalent because of its high accuracy in predicting users' interests. The efficacy of this technique mainly depends on the similarity calculation, determined by a similarity measure. However, the traditional and previously developed similarity measures in CF techniques are not able to adequately reveal the change in users' interests; therefore, an efficient measure considering time into context is proposed in this paper. The proposed method and the existing approaches are compared on the MovieLens-100k dataset, showing that the proposed method is more efficient than the comparable methods. Besides this, most of the CF approaches only focus on the historical preference of the users, but in real life, the people's preferences also change over time. Therefore, a time-based recommendation system using the proposed method is also developed in this paper. We implemented various time decay functions, i.e., exponential, convex, linear, power, etc., at various levels of the recommendation process, i.e., similarity computation, rating matrix, and prediction level. Experimental results over three real datasets (MovieLens-100k, Epinions, and Amazon Magazine Subscription) suggest that the power decay function outperforms other existing techniques when applied at the rating matrix level.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01813-z
    DOI: 10.1007/s13198-022-01813-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01813-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-022-01813-z?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Surya Kant & Tripti Mahara, 2018. "Merging user and item based collaborative filtering to alleviate data sparsity," 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. 9(1), pages 173-179, February.
    Full references (including those not matched with items on IDEAS)

    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. Taushif Anwar & V. Uma, 2021. "Comparative study of recommender system approaches and movie recommendation using collaborative filtering," 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. 12(3), pages 426-436, June.
    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. Latha, R., 2022. "Enhancing recommendation competence in nearest neighbour models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    4. 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.
    5. 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.
    6. Zhang, Cheng-Jun & Zhu, Xue-lian & Yu, Wen-bin & Liu, Jin & Chen, Ya-dang & Yao, Yu & Wang, Su-xun, 2024. "Predicting popularity of online products via collective recommendations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 641(C).
    7. Wenlong Sun & Olfa Nasraoui & Patrick Shafto, 2020. "Evolution and impact of bias in human and machine learning algorithm interaction," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.

    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:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01813-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.