Research Paper Recommendation Based on Content Similarity, Peer Reviews, Authority, and Popularity

According to the Canadian Science Publishing, there are approximately 2.5 million scientific papers published each year. The huge volume of publications can be contributed to a substantial increase in the total number of academic journals, including the increasing number of predatory or fake scienti...

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Bibliographic Details
Main Author: Ng, Yiu-Kai
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Distance measurement
Engines
Libraries
metadata
Publishing
Research paper
Web search
Online Access:Request full text
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Summary:According to the Canadian Science Publishing, there are approximately 2.5 million scientific papers published each year. The huge volume of publications can be contributed to a substantial increase in the total number of academic journals, including the increasing number of predatory or fake scientific journals, which yield high volumes of poor-quality research work. The effect of this scenario is that there is an obsolete jungle of journals to flip through in searching for high-quality and relevant references for researchers, ranging from the ones who simply look for citations to cite or latest development and knowledge in a specific scientific area of study. In solving this problem, we propose a unique, elegant research paper recommender. Besides considering the topics and contents of related publications, our recommender also examines the peer reviews, authority, and popularity of each publication to ensure its quality. Conducted empirical study shows that our recommender outperforms existing research paper recommenders and contributes to the design of searching relevant publications.
ISSN:2375-0197
DOI:10.1109/ICTAI50040.2020.00018