Scientific Paper Recommendation System

Project Code :TCMAPY1008

Objective

The objective of this project is to develop a scalable, end-to-end content-based recommendation system that suggests relevant scientific papers based on the abstract or context of a provided article. This system aims to address the challenges of information overload in scientific research, ensuring researchers can efficiently discover pertinent studies in vast datasets.

Abstract

Scientific research is currently witnessing an explosion of information, with hundreds of articles published daily, making it challenging for researchers to stay updated in their domain. Traditional methods, such as keyword matching and recommender systems, have proven time-consuming and inefficient for large datasets comprising millions of papers. This research introduces a scalable, end-to-end content-based scientific paper recommendation system. Unlike its predecessors, this system is designed to recommend research papers based on the abstract or the context of a given paper, ensuring more relevant and precise suggestions. This approach promises to revolutionize how researchers find pertinent articles, addressing the current information overload and ensuring that groundbreaking studies don't go unnoticed.

KEYWORDS: Content based recommendation algorithm.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.