JOURNAL ARTICLE

Research paper recommender system evaluation using collaborative filtering

Khalid HarunaMaizatul Akmar Ismail

Year: 2018 Journal:   AIP conference proceedings Vol: 1974 Pages: 020052-020052   Publisher: American Institute of Physics

Abstract

Several approaches have been proposed to help researchers in retrieving relevant and useful information from the cyber-ocean of information. However, the approaches assumed the academic content to be easily accessible, which is not always the case considering the copyright restrictions. Different from the existing works, we proposed a collaborative approach that leverages public contextual metadata to personalize scholarly recommendations. Our proposed approach has the ability to recommend research papers to the individual researchers regardless of the researcher expertise and research field. As demonstrated using a freely accessible dataset, our proposed approach have shown significant improvements over another baseline method.

Keywords:
Metadata Computer science Collaborative filtering Recommender system Baseline (sea) Field (mathematics) Information retrieval World Wide Web Data science

Metrics

8
Cited By
2.23
FWCI (Field Weighted Citation Impact)
21
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Recommender Systems and Techniques
Physical Sciences →  Computer Science →  Information Systems
Expert finding and Q&A systems
Physical Sciences →  Computer Science →  Information Systems
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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