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

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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.

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Data Availability

The dataset analyzed during the current study is publicly available and was sourced from Kaggle. The dataset, titled "Sales Data Analysis," can be accessed directly through the following link: https://www.kaggle.com/datasets/aemyjutt/salesdata/data. This dataset is provided under the terms of use specified by Kaggle and the dataset's original contributors. Researchers and readers are advised to comply with these terms when accessing and utilizing the dataset for their purposes.

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Acknowledgements

We would like to express our gratitude to all those who contributed to this research endeavor. We affirm that there are no conflicts of interest associated with this work. It is important to note that this project was conducted as part of a doctoral thesis and did not receive any external research funding or grants. We appreciate the support and guidance provided by our academic institutions during this journey.

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Authors and Affiliations

  1. Artificial Intelligence, Data Science and Emerging Systems Laboratory, National School of Business and Management, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Fayçal Messaoudi

  2. Artificial Intelligence, Data Science and Emerging Systems Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Manal Loukili

Authors
  1. Fayçal Messaoudi
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Contributions

F.M. and M.L. are the co-authors of this manuscript. Their respective contributions to the research and manuscript are as follows: F.M.: Conceptualization: F.M. played a key role in conceptualizing the research, defining the objectives, and outlining the study's scope. Methodology: F.M. was primarily responsible for designing the research methodology, including data collection and analysis. Writing—Original Draft Preparation: F.M. took the lead in writing the initial draft of the manuscript, including the abstract, introduction, methodology, and parts of the results and discussion sections. Data Visualization: F.M. prepared tables and figures, including the presentation of the research results. Review and Editing: F.M. contributed to reviewing and editing the manuscript. M.L.: Supervision: M.L. supervised the entire research process, providing guidance and expertise in the field of machine learning and recommendation systems. Methodology: M.L. significantly contributed to the development and implementation of the Deep Neural Collaborative Filtering (DNCF) recommendation system. Writing—Original Draft Preparation: M.L. actively participated in writing the manuscript, specifically sections related to the theoretical background, methodology, and parts of the results and discussion sections. Review and Editing: M.L. played a crucial role in reviewing and editing the manuscript for clarity and accuracy. Corresponding Author: M.L. handled correspondence related to the manuscript submission and revisions. Both authors, F.M. and M.L., reviewed and approved the final version of the manuscript for submission to the journal. All authors have read and agreed to the manuscript's content and authorship order.

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Correspondence to Manal Loukili.

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Messaoudi, F., Loukili, M. E-commerce Personalized Recommendations: a Deep Neural Collaborative Filtering Approach. Oper. Res. Forum 5, 5 (2024). https://doi.org/10.1007/s43069-023-00286-5

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