IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i2d10.1007_s10660-021-09478-9.html
   My bibliography  Save this article

Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems

Author

Listed:
  • Rahim Rashidi

    (Islamic Azad University)

  • Keyhan Khamforoosh

    (Islamic Azad University)

  • Amir Sheikhahmadi

    (Islamic Azad University)

Abstract

To offer an appropriate recommendation to customers in recommender systems, the issue of clustering and separating users with different tastes from the rest of people is of significant importance. The MkMeans + + algorithm is a technique for clustering and separating users in collaborative filtering systems. This algorithm utilizes a specific procedure for selecting the initial centroids of the clusters and has a better function compared with its similar algorithms such as kMeans + + . In this paper, MkMeans + + algorithm is combined with Firefly, Cuckoo, and Krill algorithms and new algorithms called FireflyMkMeans + + , CuckooMkMeans + + , and KrillMkMeans + + are introduced in order to specify the optimal centroid of the cluster, better separate users, and avoid local optimals. In the proposed hybrid clustering approach, the initial population of firefly, cuckoo, and krill algorithms is initialized through the solutions generated by MkMeans + + algorithm, and it makes use of the benefits of MkMeans + + as well as firefly, cuckoo, and krill algorithms. Results and implementations on both MovieLens and FilmTrust datasets indicate that the proposed algorithms can perform better than their similar algorithms in clustering and separating users with different tastes (graysheep users), and enhance the quality of clusters and the accuracy of recommendations for users with similar tastes (white users).

Suggested Citation

  • Rahim Rashidi & Keyhan Khamforoosh & Amir Sheikhahmadi, 2022. "Proposing improved meta-heuristic algorithms for clustering and separating users in the recommender systems," Electronic Commerce Research, Springer, vol. 22(2), pages 623-648, June.
  • Handle: RePEc:spr:elcore:v:22:y:2022:i:2:d:10.1007_s10660-021-09478-9
    DOI: 10.1007/s10660-021-09478-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-021-09478-9
    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/s10660-021-09478-9?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. Srivastava, Abhishek & Bala, Pradip Kumar & Kumar, Bipul, 2020. "New perspectives on gray sheep behavior in E-commerce recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    2. Honey Jindal & Shalini Agarwal & Neetu Sardana, 2018. "PowKMeans: A Hybrid Approach for Gray Sheep Users Detection and Their Recommendations," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 13(2), pages 56-69, April.
    3. Rashidi, Rahim & Khamforoosh, Keyhan & Sheikhahmadi, Amir, 2020. "An analytic approach to separate users by introducing new combinations of initial centers of clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    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. Park, Jinhee & Ahn, Hyeongjin & Kim, Dongjae & Park, Eunil, 2024. "GNN-IR: Examining graph neural networks for influencer recommendations in social media marketing," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    2. Xiao, Yan & Li, Congdong & Thürer, Matthias & Liu, Yide & Qu, Ting, 2022. "User preference mining based on fine-grained sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    3. Hallikainen, Heli & Luongo, Milena & Dhir, Amandeep & Laukkanen, Tommi, 2022. "Consequences of personalized product recommendations and price promotions in online grocery shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    4. Chinchanachokchai, Sydney & Thontirawong, Pipat & Chinchanachokchai, Punjaporn, 2021. "A tale of two recommender systems: The moderating role of consumer expertise on artificial intelligence based product recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    5. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    6. Rashidi, Rahim & Khamforoosh, Keyhan & Sheikhahmadi, Amir, 2020. "An analytic approach to separate users by introducing new combinations of initial centers of clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    7. Li, Yanbin & Zhao, Ke & Zhang, Feng, 2023. "Identification of key influencing factors to Chinese coal power enterprises transition in the context of carbon neutrality: A modified fuzzy DEMATEL approach," Energy, Elsevier, vol. 263(PA).

    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:elcore:v:22:y:2022:i:2:d:10.1007_s10660-021-09478-9. 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.