Document Type : Research ŮŽ Article
Authors
1
Ph.D. Student, Knowledge and Information Science, Islamic Azad University, Science and Research Branch of Tehran
2
Associate Professor, Knowledge and Information Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
3
Professor, Knowledge and Information Science, Science and Research Branch, Islamic azad university, Tehran, Iran
4
Associate Professor of Computer in Department of Software Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
5
Assistant Professor of Computer in Department of Software Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
10.30484/nastinfo.2024.3496.2246
Abstract
Purpose: The purpose of this study is to analyze and examine the use of data mining techniques in the recommender system of digital libraries and information centers. Analyzing the behavioral patterns of digital library users and providing detailed suggestions, the data mining approach in this system makes them unnecessary to review unrelated data during the search. Accordingly, it leads to an increase in the information requests of users and their satisfaction with the provision of digital library services.
Methods: The current research is an analytical study of cross-sectional survey type and content analysis. In this method, the required data was collected in four stages, and its output was analyzed using data mining technique. Using content analysis, as in the first stage, a list of the number of transactions of user requests for books, including user IDs, manuscript titles, manuscript identification code numbers in the digital library system (manuscript database), the organization of libraries, Museums and Documents Center of Astan Quds Razavi were investigated and the data were arranged in the form of user column and item (book) row. In the second step, the preprocessed raw data was further transformed into a user-item matrix, which is zero and one. The third stage, the data output was implemented and executed using data mining technology and the implementation of association rules and FP-Growth algorithm on RapidMiner software) and confidence (the level of confidence in the desired result) were tested. The fourth stage, the accuracy and correctness of the proposed system plan was presented.
Findings: The output of this research revealed that the association rules have a confidence level above 50% and are able to determine the user's access patterns, which is the best way to access the generated datasets by setting the minimum support level of 2% and the minimum confidence level of 95%, leading to 1081 new rules with conditional algorithms (if-then). If a user selects topics such as (science of principles, Ijtihad, tradition, etc.) during the search in the digital library software, due to the history of repeated searches by previous users with the same topics, by the recommender system, then titles related to the subject of principles of jurisprudence will be suggested. Also, the proof of the correctness of the proposed model showed that the first and last ones created from the new laws had thematic similarity with each other
Conclusion: This study showed that various data mining techniques with the application of association rules and the implementation of the FP-growth algorithm have high efficiency and accuracy and are suitable for analyzing the data of digital libraries and information centers to create recommender systems in order to predict user requests and make effective suggestions. One of its practical concepts is to provide a platform to improve the quality of two-way interaction between librarians and users in order to provide optimal and beneficial services, and also to create a suitable opportunity to improve the attitude and perspective of managers in order to provide information resources that meet the real needs of users.
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