New approach to measuring researcher expertise using cosine similarity algorithm and association rules

Abstract

This study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%.