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Article
Publication date: 30 October 2020

Nasim Ansari, Hossein Vakilimofrad, Muharram Mansoorizadeh and Mohamad Reza Amiri

This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques.

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Abstract

Purpose

This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques.

Design/methodology/approach

The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed.

Findings

The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty.

Practical implications

The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users.

Originality/value

The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

Details

Global Knowledge, Memory and Communication, vol. 70 no. 6/7
Type: Research Article
ISSN: 2514-9342

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