{"id":21281238,"url":"https://github.com/m-rishab/research-paper-recommendation","last_synced_at":"2026-04-02T02:50:05.803Z","repository":{"id":229832588,"uuid":"777755027","full_name":"m-rishab/Research-Paper-Recommendation","owner":"m-rishab","description":"This project aims to build a research paper recommendation system. 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Given a paper title as input, the system provides the top 5 recommended research papers. Additionally, it predicts the subject area of the input paper using Natural Language Processing (NLP) techniques and a Large Language Model (LLM) (Mini LM L6-V2).\n\n## Deep Learning Techniques Used\n- Natural Language Processing (NLP)\n- Large Language Model (LLM) (Mini LM L6-V2) - [Link](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2).\n\n## Sentence Transformer\nSentence Transformers is a framework that transforms sentences or text snippets into fixed-length vector representations, known as embeddings. These embeddings capture semantic meaning and are generated using pre-trained transformer models fine-tuned on large text corpora. They are useful for tasks like semantic similarity computation, text classification, and information retrieval.\n\u003cimg src=\"https://github.com/m-rishab/Research-Paper-Recommendation/assets/113618652/6078f4ab-52f9-4c25-8139-0c605ea85376\" width=\"500\" height=\"500\"\u003e\n\nThis flowchart illustrates the process flow of the research paper recommendation system, including data preprocessing, model training, and recommendation generation.\n\n## Future Improvements\n- Incorporating user feedback to enhance recommendation accuracy.\n- Expanding the dataset to cover a broader range of research domains.\n- Integrating more advanced NLP techniques for better understanding of paper content.\n\n## Dataset Used\nThe dataset used for training and evaluation is available on Kaggle. You can access it [here](https://www.kaggle.com/datasets/spsayakpaul/arxiv-paper-abstracts/data).\n\n## Demo\n\nhttps://github.com/m-rishab/Research-Paper-Recommendation/assets/113618652/0f3971b6-ebd0-439a-90b2-a168c99f054b\n\nTo run the project, follow the steps below:\n\n### How to Run This Project:\n1. Run the notebook to execute all models.\n2. After running the notebook, execute the `app.py` file using the following command:\n   `python app.py`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm-rishab%2Fresearch-paper-recommendation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fm-rishab%2Fresearch-paper-recommendation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fm-rishab%2Fresearch-paper-recommendation/lists"}