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E-commerce Personalized Recommendations: a Deep Neural Collaborative Filtering Approach

Author

Listed:
  • Fayçal Messaoudi

    (Sidi Mohamed Ben Abdellah University)

  • Manal Loukili

    (National School of Applied Sciences, Sidi Mohamed Ben Abdellah University)

Abstract

In the ever-evolving landscape of e-commerce, personalized product recommendations have emerged as a critical tool for optimizing the shopping experience and driving sales growth. This study presents a comprehensive exploration and implementation of a deep neural collaborative filtering recommendation system, aimed at fine-tuning product recommendations to meet user preferences. Our results showcase the effectiveness of the model with a precision of 0.85, indicating its ability to provide relevant suggestions, a recall score of 0.78, demonstrating successful item retrieval, and a click-through rate of 0.12, emphasizing user engagement with recommended products. While recognizing limitations related to data quality and scalability, this research highlights the potential for data-driven, machine learning-powered recommendation systems to revolutionize the e-commerce landscape. In an ever-competitive digital marketplace, advanced recommendation systems are poised to be pivotal in enhancing the shopping experience and sustaining sales growth.

Suggested Citation

  • Fayçal Messaoudi & Manal Loukili, 2024. "E-commerce Personalized Recommendations: a Deep Neural Collaborative Filtering Approach," SN Operations Research Forum, Springer, vol. 5(1), pages 1-25, March.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:1:d:10.1007_s43069-023-00286-5
    DOI: 10.1007/s43069-023-00286-5
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    References listed on IDEAS

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    1. Arodh Lal Karn & Rakshha Kumari Karna & Bhavana Raj Kondamudi & Girish Bagale & Denis A. Pustokhin & Irina V. Pustokhina & Sudhakar Sengan, 2023. "RETRACTED ARTICLE: Customer centric hybrid recommendation system for E-Commerce applications by integrating hybrid sentiment analysis," Electronic Commerce Research, Springer, vol. 23(1), pages 279-314, March.
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