High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publi...

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Bibliographic Details
Published inSystems (Basel) Vol. 11; no. 2; p. 81
Main Authors Venkatesan, Vinoth Kumar, Ramakrishna, Mahesh Thyluru, Batyuk, Anatoliy, Barna, Andrii, Havrysh, Bohdana
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2023
Subjects
Algorithms
Analysis
Artificial intelligence
Bibliographic coupling
Citation analysis
Collaboration
collaborative approach
Customization
Metadata
Methods
quality
recommender system
Recommender systems
research paper
Researchers
System effectiveness
User profiles
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Summary:The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.
ISSN:2079-8954
2079-8954
DOI:10.3390/systems11020081