Identify the effective factors of improving relevance in information retrieval in scientific social networks

Document Type : Original Article

Authors

1 faculty member of PNU

2 Associate Professor, Department of Information Science, Payame Noor University

3 Assistant Professor, Department of Information Science, Payame Noor University

Abstract

Objective: The purpose of this study is to identify the effective factors in improving relevance in information retrieval from the perspective of faculty members in the field of information science and science in the two groups of humanities and medical librarianship in the scientific social networks LinkedIn and Research Gate.

Method: This research is of applied type and has been conducted by survey method. The volume of the population considered in the three sections of humanities, medical and comprehensive library is equal to 110, 102 and 207 people, respectively, which according to Cochran's formula, the number of statistical samples is equal to 86, 81 and 135 people from the database, respectively. Have been used by LinkedIn and Research Gate.

Results: Based on the results of regression analysis, the presence of feedback with a coefficient of 0.775 has the greatest effect on its relevance and improvement in data retrieval in the LinkedIn database.

Conclusion: According to the comprehensive research model, the effective factors identified in science social networks such as information system, retrieval system can be considered as a comprehensive model to increase the relevance of information retrieval in scientific social networks.

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Sciences and Techniques of Information Management
Volume 10, Issue 3
November 2024
Pages 195-220

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