Using data mining techniques to predict and detect important features for book borrowing rate in academic libraries

Rakhmanov, Ochilbek

doi:10.1109/icecco48375.2019.9043203

Cited by 7 publications

(6 citation statements)

References 4 publications

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“…It is a tree map of possible outcomes through a series of related choices and predicts the best choice based on their cost, benefit, and probability. In [4], several tree classification methods were employed. A survey was conducted on 200 university students' library usage, and the data was used to determine the correlation between features and outcome.…”

Section: The Role Of Machine Learning In Library Systemmentioning

confidence: 99%

Analysis of Book Preferences Among Visitors in Library System

Foo,

Ng,

Haw

et al. 2024

TEM Journal

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Library is a place that contains various resources and materials. Many invaluable knowledge can be found in the library. By analysing the library’s data, it is possible to obtain information that can further improve its services. This research aims to extract information from Multimedia University (MMU) library and present insightful visualization of the information to enhance the library administration. At present, the library does not have information on the book preferences of the library users. The book preferences statistics can be relatively helpful as the library will know what books can be imported in the future. By doing so, more people will visit the library and they will have more related books to use as reference or to read. In addition, there are no existing dashboards to display information on all borrowers, no visitor. In the absence of this, this research adopts the data science methodology to determine the book preferences of library users by using machine learning techniques such as clustering and classification. Lastly, a dashboard will be developed to display all the findings which includes statistics on the visitors and book preferences.

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Section: The Role Of Machine Learning In Library Systemmentioning

confidence: 99%

Analysis of Book Preferences Among Visitors in Library System

Foo,

Ng,

Haw

et al. 2024

TEM Journal

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“…The review showed examples of exploitation in 11 papers (code U-2, 11/126 papers), whereby the data provided by patrons are used to analyse users' satisfaction (Ochilbek, 2019;Yue & Jia, 2008) or to make predictions concerning future requests (Litsey & Mauldin, 2018). Facebook posts from patrons can also be used to predict responses to different types of library posts (Gruss et al, 2020).…”

Section: Role Of the Usermentioning

confidence: 99%

“…Many of the authors address new technology as a useful tool or system (code AI-1, 51/126) or beneficial extension of human skills (code AI-2, 8/126). This viewpoint is most obvious in research and case reports that describe the concept or features of new applications for a variety of library services and operations, including acquisition and circulation (e.g., Iqbal et al, 2020;Ochilbek, 2019;Walker & Jiang, 2019), classification and subject indexing (e.g., Bethard et al, 2009;Golub et al, 2020;, resource retrieval and recommendations (e.g., Färber & Sampath, 2020;Hahn, 2019;Hahn & McDonald, 2018;Smith, 1976), or overall performance analysis (Ennis et al, 2013). These tools are described as automating some laborious or error-prone library operations, enabling faster processes in larger volumes and assisting with librarians' tasks.…”

Section: Role Of Ai (Non-human)mentioning

confidence: 99%

Understanding Artificial Intelligence in Research Libraries – Extensive Literature Review

Gasparini

Kautonen

2022

LIBER

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Artificial intelligence (AI) now forms a part of various activities in the academic world. AI will also affect how research libraries perform and carry out their services and how the various kinds of data they hold in their repositories will be used in the future. For the moment, the landscape is complex and unclear, and library personnel and leaders are uncertain about where they should lay the path ahead. This extensive literature review provides an overview of how research libraries understand, react to, and work with AI. This paper examines the roles conceived for libraries and librarians, their users, and AI. Finally, design thinking is presented as an approach to solving emerging issues with AI and opening up opportunities for this technology at a more strategic level.

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“…In another study, Ochilbek using a decision tree technique with 71.9% accuracy rate during training and 72% on test data showed that data mining could predict the behavior of library users based on borrowing transactions (Ochilbek, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

Ansari¹,

Vakilimofrad²,

Mansoorizadeh

et al. 2020

GKMC

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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.

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Using data mining techniques to predict and detect important features for book borrowing rate in academic libraries

Cited by 7 publications

References 4 publications

Analysis of Book Preferences Among Visitors in Library System

Analysis of Book Preferences Among Visitors in Library System

Understanding Artificial Intelligence in Research Libraries – Extensive Literature Review

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

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