Personalized Academic Research Paper Recommendation System
This addresses the challenge for researchers in efficiently discovering related work, but it is incremental as it applies standard methods to a specific domain.
The authors tackled the problem of researchers struggling to find relevant academic papers by proposing a personalized recommendation system that uses collaborative filtering and text similarity, and they report that it recommends good quality papers.
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.