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Implementation of a Collaborative Recommendation System Based on Multi-Clustering

Author

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  • Lili Wang

    (School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China
    Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety, Huainan 232001, China
    These authors contributed equally to this work.)

  • Sunit Mistry

    (School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China
    These authors contributed equally to this work.)

  • Abdulkadir Abdulahi Hasan

    (School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China)

  • Abdiaziz Omar Hassan

    (International School of Design, Zhejiang University, Ningbo 315100, China)

  • Yousuf Islam

    (School of Physics and Electronics, Central South University, Changsha 410083, China)

  • Frimpong Atta Junior Osei

    (Department of Computer Science, University of Oregon, Eugene, OR 97403, USA)

Abstract

The study aims to present an architecture for a recommendation system based on user items that are transformed into narrow categories. In particular, to identify the movies a user will likely watch based on their favorite items. The recommendation system focuses on the shortest connections between item correlations. The degree of attention paid to user-group relationships provides another valuable piece of information obtained by joining the sub-groups. Various relationships have been used to reduce the data sparsity problem. We reformulate the existing data into several groups of items and users. As part of the calculations and containment of activities, we consider Pearson similarity, cosine similarity, Euclidean distance, the Gaussian distribution rule, matrix factorization, EM algorithm, and k-nearest neighbors (KNN). It is also demonstrated that the proposed methods could moderate possible recommendations from diverse perspectives.

Suggested Citation

  • Lili Wang & Sunit Mistry & Abdulkadir Abdulahi Hasan & Abdiaziz Omar Hassan & Yousuf Islam & Frimpong Atta Junior Osei, 2023. "Implementation of a Collaborative Recommendation System Based on Multi-Clustering," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1346-:d:1093224
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    References listed on IDEAS

    as
    1. Bernhardt, Paul W. & Zhang, Daowen & Wang, Huixia Judy, 2015. "A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 37-53.
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