• Journal of Information Management
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• v.33 no.1
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• pp.1-17
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• 2002

This study examined several recommendation techniques to construct an effective book recommender system in a library. Experiments revealed that a hybrid recommendation technique is more effective than either collaborative filtering or content-based filtering technique in recommending books to be borrowed in an academic library setting. The recommendation technique based on association rule turned out the lowest in performance.

• Journal of the Korean Society for information Management
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• v.41 no.3
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• pp.145-169
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• 2024

This study examined university library services for students, including international students, and the AI-based information services provided by libraries. Additionally, the standards of Korean language education for international students were investigated. Based on the analysis of library services and these standards, a book recommendation system for learning Korean was developed using ChatGPT. The recommendation results from three training datasets were evaluated for recommendation precision. The results of the chatbot's book recommendations based on the 13 test questions were evaluated by recommendation precision. The comparison of the recommendation precision showed that the chatbot using the combined dataset was more successful in recommending all relevant books compared to the individual datasets. This study serves as an example of an effective approach to utilizing artificial intelligence technology for user services in university libraries.

• Journal of Korean Library and Information Science Society
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• v.47 no.1
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• pp.279-303
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• 2016

This study proposes the Book-Curation service as part of the information service offered through school library websites. Also, this study aims to establish recommendation standards for curation prior to detailed system planning. For such service, the following tasks were carried out. First, the list of recommended books of existing systems were analyzed to identify the attributes that can be used for recommendation in the user and book information. Second, the analyzed attributes were utilized to establish 12 recommendation standards. Finally, a survey was carried out to identify the user preferences as to each standards. The results are as follows. First, the majority of students responded that curation service is necessary for using a library. Second, the top three standards are as follows: "best lending books based on the keywords of individual users"; "best lending books of the same year students"; "best lending books on the textbook-related reference booklist".

• Journal of Korean Library and Information Science Society
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• v.54 no.2
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• pp.23-42
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• 2023

The library tour program is a new type of cultural program that was first introduced and operated by J City, and library tourists travel to specialized libraries in the city according to a set course and experience various experiences. This study aims to build a customized course recommendation model that considers the characteristics of individual participants in addition to the existing fixed group travel format so that more users can enjoy the opportunity to participate in library tours. To this end, the characteristics of library travelers were categorized to establish traveler personas, and library evaluation items and evaluation criteria were established accordingly. We selected 22 libraries targeted by the library travel program and measured library data through actual visits. Based on the collected data, we derived the characteristics of suitable libraries and developed a persona-based library tour course recommendation model using a decision tree algorithm. To demonstrate the feasibility of the proposed recommendation model, we build a mobile application mockup, and conducted user evaluations with actual library users to identify satisfaction and improvements to the developed model.

• Journal of the Korean Society for information Management
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• v.39 no.3
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• pp.1-22
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• 2022

The purpose of this study is to propose a book recommendation system that can be used in school libraries. The book recommendation system applies an algorithm based on association rules using DLS lending data and is designed to provide personalized book recommendation services to school library users. For this purpose, association rules based on the Apriori algorithm and betweenness centrality analysis were applied and detailed functions such as descriptive statistics, generation of association rules, student-centered recommendation, and book-centered recommendation were materialized. Subsequently, opinions on the use of the book recommendation system were investigated through in-depth interviews with teacher librarians. As a result of the investigation, opinions on the necessity and difficulty of book recommendation, student responses, differences from existing recommendation methods, utilization methods, and improvements were confirmed and based on this, the following discussions were proposed. First, it is necessary to provide long-term lending data to understand the characteristics of each school. Second, it is necessary to discuss the data integration plan by region or school characteristics. Third, It is necessary to establish a book recommendation system provided by the Comprehensive Support System for Reading Education. Based on the contents proposed in this study, it is expected that various discussions will be made on the application of a personalization recommendation system that can be used in the school library in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.193-198
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• 2023

We study a learner-customized lecture recommendation project using deep learning. Recommendation systems can be easily found on the web and apps, and examples using this feature include recommending feature videos by clicking users and advertising items in areas of interest to users on SNS. In this study, the sentence similarity Word2Vec was mainly used to filter twice, and the course was recommended through the Surprise library. With this system, it provides users with the desired classification of course data conveniently and conveniently. Surprise Library is a Python scikit-learn-based library that is conveniently used in recommendation systems. By analyzing the data, the system is implemented at a high speed, and deeper learning is used to implement more precise results through course steps. When a user enters a keyword of interest, similarity between the keyword and the course title is executed, and similarity with the extracted video data and voice text is executed, and the highest ranking video data is recommended through the Surprise Library.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.175-186
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• 2021

The roles and functions of domestic public libraries are diversifying, but various problems have emerged due to internally biased book lending. In addition, due to the 4th Industrial Revolution, public libraries have introduced a book recommendation system focusing on popular books, but the variety of books that users can access is limited. Therefore, in this study, the public library unborrowed book recommendation system was implemented limiting its spatial scope to Duryu Library in Daegu City to enhance the satisfaction of public library users, by using the loan records data (213,093 cases), user information (35,561 people), etc. and utilizing methods like cluster analysis, topic modeling, content-based filtering recommendation algorithm, and conducted a survey on actual users' satisfaction to present the possibility and implications of the unborrowed book recommendation system. As a result of the analysis, the majority of users responded with high satisfaction, and was able to find the satisfaction was relatively high in the class classified by specific gender, age, occupation, and usual reading. Through the results of this study, it is expected that some problems such as biased book lending and reduced operational efficiency of public libraries can be improved, and limitations of the study was also presented.

• Journal of Korean Library and Information Science Society
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• v.52 no.3
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• pp.287-311
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• 2021

It is not easy for information users to find books that are suitable for them in a knowledge information society. There is a growing need for libraries to break away from traditional services and provide user-tailored recommendation services, but there are few qualitative studies on user satisfaction so far. In this study, a user-customized book recommendation was performed by applying Apriori, a correlation analysis algorithm, and satisfaction factors were analyzed in depth through interviews. The experimental data was the loan data of 100 people who used the most frequently used loan data for 10 years from 2009 to 2019 of the S library in Seoul. The interviewees of the experiment were those who could be interviewed in depth. After the correlation analysis, the concepts and categories derived by analyzing the interview data were 59 concepts, 6 sub-categories, and 2 upper categories, respectively. The upper categories were 'reading' and 'book recommendation service'. In the 'reading' category, there were 16 concepts of motivation for reading, 8 concepts of preferred books, and 12 concepts of expected effects. Also, in the category of 'reading recommendation service', there were 10 'reflection factors', 4 'reflection methods', and 9 'satisfaction factors'.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.4
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• pp.95-119
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• 2020

The study examined the behavior of infant's caregivers using public libraries, measured the level of awareness of infant and toddler services in the library to determine the factors affecting their revisit or recommendation of use to others, and examined the impact of such recognition on the use behavior, revisit and willingness to recommend others. The study surveyed 146 caregivers of infants who use four public libraries in Gwangju Metropolitan City on demographic characteristics, library use behavior, level of awareness of library services, library revisit and recommendation to others. Analysis of this survey shows that the factors that influence the caregivers' revisit of the library or their desire to recommend to others through their caregivers' use of the library are the factors that influence the expansion of information data, the stability of space facilities, the accessibility of space facilities, and the ease of participation in programs among the various service factors provided by the library rather than the personal factors of infants and toddlers' caregivers.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.501-518
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• 2017

Reducing the time it takes for researchers to acquire knowledge and introduce them into research activities can be regarded as an indispensable factor in improving the productivity of research. The purpose of this research is to cluster the information usage patterns of KOSEN users and to suggest optimization method of personalized recommendation service algorithm for grouped users. Based on user research activities and usage information, after identifying appropriate services and contents, we applied a Spark based big data analysis technology to derive a personal recommendation algorithm. Individual recommendation algorithms can save time to search for user information and can help to find appropriate information.