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BERT-enhanced sentiment analysis for personalized e-commerce recommendations

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Abstract

Recommendation systems (RS) play a crucial role in enhancing conversion rates in e-commerce by offering personalized product recommendations based on customer preferences. However, traditional RS heavily rely on numerical ratings, which might not fully capture the subtle nuances of user preferences. To overcome this limitation, the integration of textual data, such as reviews using sentiment analysis (SA), has gained considerable significance. Nevertheless, effectively analyzing and comprehending unstructured review data presents its own set of challenges. In this work, we propose a novel RS that synergizes collaborative filtering with sentiment analysis to deliver precise and individualized recommendations. Our approach encompasses three main steps: (1) Developing a BERT fine-tuned model for accurate sentiment classification, (2) Creating a hybrid collaborative filtering-based Recommendation Model, and (3) Improving the product selection process in the RS using BERT insights for enhanced recommendation accuracy in the e-commerce domain. Notably, our SA model exhibits remarkable accuracy, achieving 91%, and outperforming state-of-the-art models on a benchmark dataset. Through extensive experimentation and evaluation, we demonstrate that our method significantly improves the accuracy and personalization of the RS, thereby providing customers with a tailored and reliable recommendation service in the e-commerce domain.

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Authors and Affiliations

  1. IPI Laboratory, Department of Mathematics and Computer Science, High Normal School, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Ikram Karabila, Nossayba Darraz & Mostafa EL Mallahi

  2. MASI Laboratory, Computer Science Department, Polydisciplinary Faculty, Mohammed First University, Nador, Morocco

    Anas EL-Ansari

  3. MASI Laboratory, Mohammed First University, Higher School of Technology, Nador, Morocco

    Nabil Alami

Authors
  1. Ikram Karabila
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  2. Nossayba Darraz
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  3. Anas EL-Ansari
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  4. Nabil Alami
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  5. Mostafa EL Mallahi
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Correspondence to Ikram Karabila.

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Karabila, I., Darraz, N., EL-Ansari, A. et al. BERT-enhanced sentiment analysis for personalized e-commerce recommendations. Multimed Tools Appl 83, 56463–56488 (2024). https://doi.org/10.1007/s11042-023-17689-5

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  • DOI: https://doi.org/10.1007/s11042-023-17689-5

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