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This paper proposes an AI-based system to classify research papers in the relevant academic domain and recommend equally similar papers based on the title and essence of a given paper. Using a semantic scholar and scholar API, the system automatically reduces the subject-field classification and relevance-based paper recommendations, leaving the necessary manual efforts in literature reviews. This system facilitates metadata such as quotes, author details and intensive explorations like direct paper URL. A user -friendly web interface enables spontaneous interactions, enhancing educational productivity by accelerating the discovery of literature and improving the search accuracy.

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Automated Research Paper Classification and Recommendation System for Efficient Literature Discovery

  • 会议论文

  • 发表日期：

    2026年

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  • Sree Lakshmi Done,
  • Jananee Schandra,
  • G. Rishitha,
  • M. Jasmita Rani

摘要

This paper proposes an AI-based system to classify research papers in the relevant academic domain and recommend equally similar papers based on the title and essence of a given paper. Using a semantic scholar and scholar API, the system automatically reduces the subject-field classification and relevance-based paper recommendations, leaving the necessary manual efforts in literature reviews. This system facilitates metadata such as quotes, author details and intensive explorations like direct paper URL. A user -friendly web interface enables spontaneous interactions, enhancing educational productivity by accelerating the discovery of literature and improving the search accuracy.