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From e-commerce to entertainment, recommender systems have become crucial in improving user experience. This survey paper examines the advanced recommendation systems with respect to marking methodologies like content-based filtering, collaborative filtering, hybrid methods and opinion mining. In addition, the paper discusses novel designs that incorporate knowledge graphs, deep neural networks, and word embeddings. This survey summarizes the merits and demerits of these methods, thoroughly marking the state-of-the-art recommendation systems. Open problems are defined and directions to future personalized recommendation research are suggested in this work.

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A Survey on Advanced Recommendation Systems: Content-Based Filtering, Collaborative Filtering, Hybrid and Opinion Mining Approaches

  • 会议论文

  • 发表日期：

    2026年

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  • Ashwini Matange,
  • Wafiya Mulla,
  • Sakshi Rane,
  • Unnati Vaidya,
  • Pranav Utage,
  • Raj Firke,
  • Dipesh Walte

摘要

From e-commerce to entertainment, recommender systems have become crucial in improving user experience. This survey paper examines the advanced recommendation systems with respect to marking methodologies like content-based filtering, collaborative filtering, hybrid methods and opinion mining. In addition, the paper discusses novel designs that incorporate knowledge graphs, deep neural networks, and word embeddings. This survey summarizes the merits and demerits of these methods, thoroughly marking the state-of-the-art recommendation systems. Open problems are defined and directions to future personalized recommendation research are suggested in this work.