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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
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    References listed on IDEAS

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    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).
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