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In recent years, deep learning-based approaches have garnered significant attention in the realm of sequential recommendation. To elucidate the current trends and advancements in this field, we have systematically reviewed and classified pertinent works (especially in the past three years). This paper describes the concept of sequential recommendation, categorizes the literature according to the overall recommendation process, and evaluates key methods influencing model performance. Additionally, we analyze the role of these factors and provide a comprehensive overview of emerging challenges and future research directions. Our study offers valuable insights into the evolving landscape of deep learning-based sequential recommender systems.

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A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and Directions

  • 会议论文

  • 发表日期：

    2025年

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  • Yuchen Liu,
  • Jianpeng Qi,
  • Yanwei Yu

摘要

In recent years, deep learning-based approaches have garnered significant attention in the realm of sequential recommendation. To elucidate the current trends and advancements in this field, we have systematically reviewed and classified pertinent works (especially in the past three years). This paper describes the concept of sequential recommendation, categorizes the literature according to the overall recommendation process, and evaluates key methods influencing model performance. Additionally, we analyze the role of these factors and provide a comprehensive overview of emerging challenges and future research directions. Our study offers valuable insights into the evolving landscape of deep learning-based sequential recommender systems.