Research Paper Recommendation System

Objective

This project aims to build a research paper recommendation system. 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).

Deep Learning Techniques Used

• Natural Language Processing (NLP)
• Large Language Model (LLM) (Mini LM L6-V2) - Link.

Sentence Transformer

Sentence 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.

This flowchart illustrates the process flow of the research paper recommendation system, including data preprocessing, model training, and recommendation generation.

Future Improvements

• Incorporating user feedback to enhance recommendation accuracy.
• Expanding the dataset to cover a broader range of research domains.
• Integrating more advanced NLP techniques for better understanding of paper content.

Dataset Used

The dataset used for training and evaluation is available on Kaggle. You can access it here.

Demo

Research.Papers.Recommendation.-.Google.Chrome.2024-03-26.19-22-53.mp4

To run the project, follow the steps below:

How to Run This Project:

1. Run the notebook to execute all models.
2. After running the notebook, execute the app.py file using the following command: python app.py