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A collaborative approach for research paper recommender system
- Source :
- PLoS ONE, Vol 12, Iss 10, p e0184516 (2017), PLoS ONE
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.
- Subjects :
- Computer and Information Sciences
Science Policy
Computer science
Social Sciences
lcsh:Medicine
02 engineering and technology
Recommender system
Research and Analysis Methods
Bioinformatics
Field (computer science)
Database and Informatics Methods
Open Science
Sociology
Open Data
Citation analysis
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Collaborative filtering
Computer Networks
Cooperative Behavior
lcsh:Science
Publishing
Internet
Metadata
Social Research
Multidisciplinary
business.industry
Applied Mathematics
Simulation and Modeling
Research
lcsh:R
Novelty
Research Assessment
Data science
Open data
Social Networks
Citation Analysis
Information Retrieval
Physical Sciences
lcsh:Q
020201 artificial intelligence & image processing
The Internet
business
Mathematics
Algorithms
Network Analysis
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....609418009e45579304e7a3bce0e2c5c4