IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p2947-d1059599.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/2947/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/2947/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. Zhang, Shujuan & Jin, Zhen & Zhang, Juan, 2016. "The dynamical modeling and simulation analysis of the recommendation on the user–movie network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 310-319.
    2. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    3. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    4. Jun Wu & Yuanyuan Li & Li Shi & Liping Yang & Xiaxia Niu & Wen Zhang, 2022. "ReRec: A Divide-and-Conquer Approach to Recommendation Based on Repeat Purchase Behaviors of Users in Community E-Commerce," Mathematics, MDPI, vol. 10(2), pages 1-20, January.
    5. Moradi, Mehdi & Parsa, Saeed, 2019. "An evolutionary method for community detection using a novel local search strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 457-475.
    6. Rongheng Lin & Zezhou Ye & Yingying Zhao, 2019. "OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering," Energies, MDPI, vol. 12(14), pages 1-17, July.
    7. Zare, Hadi & Nikooie Pour, Mina Abd & Moradi, Parham, 2019. "Enhanced recommender system using predictive network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 322-337.
    8. Lin Yu & Xiaodan Guo & Dongdong Zhou & Jie Zhang, 2024. "A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks," Mathematics, MDPI, vol. 12(10), pages 1-20, May.
    9. Juyeon Son & Wonyoung Choi & Sang-Min Choi, 2020. "Trust information network in social Internet of things using trust-aware recommender systems," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.

    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:gam:jsusta:v:15:y:2023:i:4:p:2947-:d:1059599. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.