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<p>With the rapid growth of online information, the recommender system (RSs) has become essential tools for filtering information and generating personalized recommendations for customers. These systems analyze individual user preferences, past reviews, and product ratings to enhance decision-making in the e-commerce sector. In this comprehensive review, several research articles published between 2020 and 2025 have been reviewed from various journals and conferences, covering multiple dimensions of recommender systems. The study emphasizes emerging techniques aimed at improving system performance. It begins by providing background information on recommender systems, including existing literature and prevailing research challenges. Furthermore, it explores cutting-edge methodologies such as machine learning, deep learning, large language models, and conversational recommender systems that are contributing to the evolution of RSs. Beyond traditional approaches, this review addresses significant research gaps and identifies key aspects influencing RS platforms. The ultimate goal of this study is to present a comprehensive understanding of the current state and future direction of recommender systems in the e-commerce industry.</p>

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Emerging trends of recommender system for e-commerce: a comprehensive review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月31日

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  • Chour Singh Rajpoot,
  • Varun Tiwari,
  • Santosh Kumar Vishwakarma

摘要

With the rapid growth of online information, the recommender system (RSs) has become essential tools for filtering information and generating personalized recommendations for customers. These systems analyze individual user preferences, past reviews, and product ratings to enhance decision-making in the e-commerce sector. In this comprehensive review, several research articles published between 2020 and 2025 have been reviewed from various journals and conferences, covering multiple dimensions of recommender systems. The study emphasizes emerging techniques aimed at improving system performance. It begins by providing background information on recommender systems, including existing literature and prevailing research challenges. Furthermore, it explores cutting-edge methodologies such as machine learning, deep learning, large language models, and conversational recommender systems that are contributing to the evolution of RSs. Beyond traditional approaches, this review addresses significant research gaps and identifies key aspects influencing RS platforms. The ultimate goal of this study is to present a comprehensive understanding of the current state and future direction of recommender systems in the e-commerce industry.