Research paper recommendation system based on multiple features from citation network

Datos Bibliográficos

ID21443531
AutoresTayyaba Kanwal (International Islamic University, Islamabad), Tehmina Amjad (0000-0003-1201-498X, International Islamic University, Islamabad, autor de correspondencia)
Año2024
Volumen129
Número9
Páginas5493-5531
Fecha de publicación2024-09-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaScientometrics (JOURNAL)
Identificadores de la revistaISSN: 0138-9130 • E-ISSN: 1588-2861
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11192-024-05109-w
OpenAlexW4401372619
IdiomaEN
Citas recibidas2
Referencias citadas32

With tremendous growth in the volume of published scholarly work, it becomes quite difficult for researchers to find appropriate documents relevant to their research topic. Many research paper recommendation approaches have been proposed and implemented which include collaborative filtering, content-based, metadata, link-based and multi-level citation network. In this research, a novel Research paper Recommendation system is proposed by integrating Multiple Features (RRMF). RRMF constructs a multi-level citation network and collaboration network of authors for feature integration. The structure and semantic based relationships are identified from the citation network whereas key authors are extracted from collaboration network for the study. For experimentation and analysis, AMiner v12 DBLP-Citation Network is used that covers 4,894,081 academic papers and 45,564,149 citation relationships. The information retrieval metrices including Mean Average Precision, Mean Reciprocal Rank and Normalized Discounted Cumulative Gain are used for evaluating the performance of proposed system. The research results of proposed approach RRMF are compared with baseline Multilevel Simultaneous Citation Network (MSCN) and Google Scholar. Consequently, comparison of RRMF showed 87% better recommendations than the traditional MSCN and Google Scholar

Baseline (sea) · Citation · Data mining · Data science · Feature (linguistics) · Information retrieval · Key (lock) · Mean reciprocal rank · Metadata · Network analysis · Rank (graph theory) · Reciprocal · World Wide Web · Computer Science · Expert finding and Q&A systems · Mathematics · Recommender Systems and Techniques · Topic Modeling