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<p>The rapid growth of e-commerce has highlighted the need for enhanced customised services and operational efficiency. The presented research presents a novel hybrid framework that combines Collaborative Filtering (CF), Matrix Factorisation (MF), and Reinforcement Learning (RL) to enhance the consumer experience and streamline backend operations. By leveraging historical data, this approach provides a dynamic and adaptive system that does not rely on real-time data. While CF and MF are effective at creating personalised recommendations, RL introduces adaptive pricing strategies that take into account market demand and competitor actions, outperforming static models. In addition, Natural Language Processing (NLP) is used to analyse customer feedback, providing sentiment insights that improve customer service. AI-powered automation also optimises supply chain management by improving inventory forecasting, lowering costs, and increasing efficiency. Experimental results on the Retailrocket, Instacart, and Amazon Reviews datasets demonstrate that the hybrid model outperforms traditional approaches. On Retailrocket, the model outperformed baseline models by converting 19.1% and retaining 28.5% of customers. Profitability increased by 6.3%, while the model reduced RMSE to 1.05 and MAE to 0.27 on Retailrocket. These findings show the framework’s ability to improve both personalised recommendations and business operations, making it a scalable solution for e-commerce platforms.</p>

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A scalable hybrid framework for boosting customer experience and operational efficiency in e-commerce

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

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  • Haowei Liu,
  • Farah Raihana Ismail,
  • Weihang Zhang,
  • Ping Zou,
  • Tarak Hussain,
  • Yogesh Kumar Sharma,
  • Umesh Kumar Lilhore,
  • Sarita Simaiya,
  • Lidia Gosy Tekeste

摘要

The rapid growth of e-commerce has highlighted the need for enhanced customised services and operational efficiency. The presented research presents a novel hybrid framework that combines Collaborative Filtering (CF), Matrix Factorisation (MF), and Reinforcement Learning (RL) to enhance the consumer experience and streamline backend operations. By leveraging historical data, this approach provides a dynamic and adaptive system that does not rely on real-time data. While CF and MF are effective at creating personalised recommendations, RL introduces adaptive pricing strategies that take into account market demand and competitor actions, outperforming static models. In addition, Natural Language Processing (NLP) is used to analyse customer feedback, providing sentiment insights that improve customer service. AI-powered automation also optimises supply chain management by improving inventory forecasting, lowering costs, and increasing efficiency. Experimental results on the Retailrocket, Instacart, and Amazon Reviews datasets demonstrate that the hybrid model outperforms traditional approaches. On Retailrocket, the model outperformed baseline models by converting 19.1% and retaining 28.5% of customers. Profitability increased by 6.3%, while the model reduced RMSE to 1.05 and MAE to 0.27 on Retailrocket. These findings show the framework’s ability to improve both personalised recommendations and business operations, making it a scalable solution for e-commerce platforms.