Recommendation System for Newly Published Research Papers using Joint Multi Relation Model

Abstract

Fetching recently published research papers is a time consuming and tedious process for young researchers and even for experts. In real time searching papers based on keywords and queries in search engines may not result apt or proper research papers that the user searched for. Because these research papers lack proper links and citations that help to find the most concerned papers. To overcome this problem a hybrid model is proposed with time constraint that combines the Text mining and Recommendation algorithm. The preference of papers and articles are jointly modeled with matrices sharing common dimensions of researchers and papers. The initial process starts with text mining algorithm that matches keywords with the data available in web pages. The post process consists of recommendation algorithms with latent matrix factorization and tensor matrix with similar preferences in a dimensional space. This paper explains the hybrid experimental model that helps users to fetch the most recent and relevant paper in a short period of time