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Research-Papers-Recommendation-System-and-Subject-Area-Prediction-Using-Deep-Learning-and-LLMS

Overview

This repository hosts a machine learning project that encompasses two key functionalities: a research papers recommendation system and subject area prediction. The goal of the project is to provide users with tailored recommendations based on their preferences and to predict the subject area of research papers.

Features

Research Papers Recommendation System

Leveraging sentence transformers using sentence embedding with cosine similarity techniques to recommend research papers based on user preferences and similarities to other vectors.

Deep Learning Model:

Implementing a deep learning model to capture complex patterns implementing MLP to predict subject area of a paper.

Subject Area Prediction

Text Classification: Utilizing natural language processing techniques for classifying research papers into subject areas.

How to use:

Prepare your research papers dataset with titles, abstracts, and corresponding subject areas. Text Classification:

Utilize the notebook to train models for predicting subject areas.

Results

Research Papers Recommendation Recommending top K papers. The deep learning model improved recommendations, yielding an accuracy of 99%. Subject Area Prediction

Author

Acknowledgments Mention any credits or acknowledgments for third-party tools, libraries, or resources used in the project.

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