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OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System

Author

Listed:
  • Yonis Gulzar

    (Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

  • Ali A. Alwan

    (School of Theoretical and Applied Science, Ramapo College of New Jersey, Mahwah, NJ 07430, USA)

  • Radhwan M. Abdullah

    (Division of Basic Sciences, College of Agriculture and Forestry, University of Mosul, Mosul 41002, Iraq)

  • Abedallah Zaid Abualkishik

    (College of Computer Information Technology, American University in the Emirates, Dubai 503000, United Arab Emirates)

  • Mohamed Oumrani

    (Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Gombak 53100, Selangor, Malaysia)

Abstract

The industry of e-commerce (EC) has become more popular and creates tremendous business opportunities for many firms. Modern societies are gradually shifting towards convenient online shopping as a result of the emergence of EC. The rapid growth in the volume of the data puts users in a big challenge when purchasing products that best meet their preferences. The reason for this is that people will be overwhelmed with many similar products with different brands, prices, and ratings. Consequently, they will be unable to make the best decision about what to purchase. Various studies on recommendation systems have been reported in the literature, concentrating on the issues of cold-start and data sparsity, which are among the most common challenges in recommendation systems. This study attempts to examine a new clustering technique named the Ordered Clustering-based Algorithm (OCA), with the aim of reducing the impact of the cold-start and the data sparsity problems in EC recommendation systems. A comprehensive review of data clustering techniques has been conducted, to discuss and examine these data clustering techniques. The OCA attempts to exploit the collaborative filtering strategy for e-commerce recommendation systems to cluster users based on their similarities in preferences. Several experiments have been conducted over a real-world e-commerce data set to evaluate the efficiency and the effectiveness of the proposed solution. The results of the experiments confirmed that OCA outperforms the previous approaches, achieving higher percentages of Precision ( P ), Recall ( R ), and F-measure ( F ).

Suggested Citation

  • Yonis Gulzar & Ali A. Alwan & Radhwan M. Abdullah & Abedallah Zaid Abualkishik & Mohamed Oumrani, 2023. "OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:2947-:d:1059599
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
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