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<p>The explosive growth of academic literature is creating major challenges for researchers who are trying to sort through the overwhelming number of publications available. This study proposes an easy-to-use hybrid recommendation system, to help with identifying and accessing relevant research papers. Our system integrated three different approaches - content-based filtering which matches papers based on similar terms, the use of BERT for semantic analysis to encompass more specific structural connections, and knowledge graph methods that show how words relate structurally inside different research communities. The system has been implemented as a simple web application where researchers can enter a title or ID of a research paper, and receive a list of recommended items, with a relevance score. Our framework also suggested an interactive visualization feature to help users explore the relationships of papers, authors, and topics visually. The system’s output was measured using evaluation metrics which support the systems usefulness with a Precision of 0.855, Recall of 0.78, and F1-Score of 0.811 - and a Mean Reciprocal Rank (MRR) value of 0.92, and Normalized Discounted Cumulative Gain (NDCG) value of 0.88 providing confidence that relevant papers would generally be located at the top of the recommended list. When bridging the gap between algorithmically driven recommendations and intuitive exploration, our system can allow researchers to discover relevant literature more efficiently than typical keyword-based search processes.</p>

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VRD: Visual Research Discovery—A Hybrid Framework Integrating Semantic Search and Interactive Visualization

  • 期刊论文

  • 发表日期：

    2025年11月12日

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  • Neha Yadav,
  • Dhanalekshmi Gopinathan

摘要

The explosive growth of academic literature is creating major challenges for researchers who are trying to sort through the overwhelming number of publications available. This study proposes an easy-to-use hybrid recommendation system, to help with identifying and accessing relevant research papers. Our system integrated three different approaches - content-based filtering which matches papers based on similar terms, the use of BERT for semantic analysis to encompass more specific structural connections, and knowledge graph methods that show how words relate structurally inside different research communities. The system has been implemented as a simple web application where researchers can enter a title or ID of a research paper, and receive a list of recommended items, with a relevance score. Our framework also suggested an interactive visualization feature to help users explore the relationships of papers, authors, and topics visually. The system’s output was measured using evaluation metrics which support the systems usefulness with a Precision of 0.855, Recall of 0.78, and F1-Score of 0.811 - and a Mean Reciprocal Rank (MRR) value of 0.92, and Normalized Discounted Cumulative Gain (NDCG) value of 0.88 providing confidence that relevant papers would generally be located at the top of the recommended list. When bridging the gap between algorithmically driven recommendations and intuitive exploration, our system can allow researchers to discover relevant literature more efficiently than typical keyword-based search processes.