Personalized Academic Research Paper Recommendation System

TL;DR

This paper presents a personalized research paper recommendation system that uses web crawling, text similarity, and collaborative filtering to suggest relevant articles to individual researchers, aiming to improve discovery efficiency.

Contribution

It introduces a novel combination of web crawling, text similarity, and collaborative filtering for personalized academic paper recommendations.

Findings

System effectively recommends relevant research papers

High quality of recommendations demonstrated through evaluation

Combines multiple techniques for improved personalization

Abstract

A huge number of academic papers are coming out from a lot of conferences and journals these days. In these circumstances, most researchers rely on key-based search or browsing through proceedings of top conferences and journals to find their related work. To ease this difficulty, we propose a Personalized Academic Research Paper Recommendation System, which recommends related articles, for each researcher, that may be interesting to her/him. In this paper, we first introduce our web crawler to retrieve research papers from the web. Then, we define similarity between two research papers based on the text similarity between them. Finally, we propose our recommender system developed using collaborative filtering methods. Our evaluation results demonstrate that our system recommends good quality research papers.