• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.4
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• pp.101-124
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• 2023

Book recommendations in libraries can be used as a tool to help users with vague needs browse and select books. Therefore, libraries spend a lot of time and effort to introduce various materials to their users and recommend suitable books. Meanwhile, various organizations other than libraries also publish recommended reading lists, and these lists reflect the intentions of the entity that selects the recommended books. The purpose of this study is finding out how the recommended book lists of libraries differ from the recommended reading lists of non-library organizations. To achieve this, the study limited the scope to 'teenagers', who are the main target audience for book recommendations in many organizations, and compared the recommended book lists of libraries and non-library organizations in terms of (1) the selection criteria for recommended books, (2) the characteristics of recommended books, and (3) the way of providing recommended book lists. Through this analysis, the study identified the characteristics and limitations of library recommended book lists and discussed areas for improvement.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.153-171
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• 2017

Many readers tend to read books of a specific author and to expand their reading areas according to the author. This study chose Edgar Allan Poe and analyzed the image of the author using co-recommended authors and books by other readers. The frequencies of co-occurred authors and books were investigated and the relations of authors and books were analyzed with network analysis methods. As a result, genre images of Poe, related authors, and related books are discovered. This study also suggested the methods to identify the image of a author, related author groups, and related books for libraries' reading programs and book curation.

• Journal of Korean Library and Information Science Society
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• v.52 no.4
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• pp.205-228
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• 2021

This study focuses on the development of checklist on articulating reading appreciation, which will be used as the initial data for book recommendation for library users. As reading comprehension is prerequisite for reading appreciation, researchers analyzed research articles on reading comprehension to find out the core factors on reading comprehension and categorize them. Studies on reader response theory and literacy education were also examined: key words and phrases that will stimulate readers' response to reading were extracted and formed as questions. These questions were reviewed by experts on reading education. The final checklist consists of 14 questions - 4 questions on literal·inferential comprehension, 3 on evaluative comprehension, and 3 on appreciative comprehension.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.154-160
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• 2023

Recently, with the activation of eBook services, books are being published simultaneously as physical books and digitized eBooks. Paper books are more expensive than e-books due to printing and distribution costs, so demand for relatively inexpensive e-books is increasing. There are cases where previously published physical books cannot be digitized due to the circumstances of the publisher or author, so there is a movement among individual users to digitize books that have been published for a long time. However, existing research has only studied the advancement of the pre-processing process that can improve text recognition before applying OCR technology, and there are limitations to digitization depending on the condition of the book. Therefore, support for book digitization services depending on the condition of the physical book is needed. need. In this paper, we propose a method to support digitalization services according to the status of physical books held by book owners. Create images by scanning books and extract text information from the images through OCR. We propose a method to recover text that cannot be extracted depending on the state of the book using BERT, a natural language processing deep learning model. As a result, it was confirmed that the recovery method using BERT is superior when compared to RNN, which is widely used in recommendation technology.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.1-14
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• 2006

This study proposed a customer preference estimation model for production recommendation and a method to enhance the performance of product recommendation using the estimated customer preference information. That is, we suggested customer preference estimation model to estimate exactly customer's product preference with his behavior. This model shows the relationship of customer's behaviors with his preferences. The proposed estimation model is optimized by learning the relative weights of customer's behavior variables to have an effect on his preference and enables to estimate exactly his preference. To validate our proposed models, we collected virtual book store data and then made a comparative analysis of our proposed models and a benchmark model in terms of performance results of collaborative filtering for product recommendation. The benchmark model means a prior preference weighting model. The results of our empirical analysis showed that our proposed model performed better results than the benchmark model.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.3
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• pp.41-69
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• 2020

The purpose of this study is to design and develop a alternative material recommendation system using automatic classification, based on user preference. Details of usage data by users from DREAM was analysed in order to develop the way of a method on selecting proper alternative material, and then the data by user preference were allocated under each category of 10 KDC categories. The keyword, selected from the title of users' usage data from a certain period of time, were divided into 10 subject categories and ranked by the order of frequency of appearance. Books including high frequency of the keyword in title can be selected as a preferred target for producing alternative materials. Lastly, a dynamic linkage for sharing usage data among National Library for the Disabled and other libraries is proposed to produce more proper alternative materials, based on user preference.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1177-1179
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• 2015

최근, 수많은 종류의 도서가 출판되고 있다. 또한 도서의 분야와 장르, 종류가 다양해지고 그 양 역시도 방대해지고 있다. 이러한 상황에서 사용자에게 적절한 도서를 고르기란 어려운 일이다. 본 논문에서는 보다 편리하고 적절한 도서 선택을 위해 도서추천시스템을 제안한다. 사용자의 나이와 성별, 국내/외도서, 선호 장르에 가중치를 부여하고 협업필터링을 사용하는 추천 시스템을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.363-364
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• 2023

카테고리별 베스트셀러를 통해 트렌드 파악 및 사용자 맞춤형 도서 추천을 위해 카테고리별로 도서 데이터를 수집하고, 대용량 데이터인 위키피디어 데이터를 이용하여 워드임베딩 모델을 구축한다. 도서 데이터에 대한 키워드 분석 및 LDA 주제분석 기법에 의해 카테고리별 핵심 단어 분석을 통해 도서 트렌드를 파악하고, 사용자 맞춤형 도서 정보 제공 및 도서를 추천하는 기능을 구현한다.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.431-441
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• 2008

The academic information analysis service is including automatic recommendation of reviewers and analysis of researchers' accomplishments. The service of recommendation of reviewers should be processed in a transparent, fair and accountable way. When selecting reviewers, the following information must be considered: subject of project, reviewer's maj or, expertness of reviewer, relationship between applicant and reviewer. The analysis service of researchers' accomplishments is providing statistic information of researcher, institution and location based on accomplishments including book, article, patent, report and work of art. In order to support these services, we designed ontology for academic information, converted legacy data to RDF triples, expanded knowledge appropriate to services using OntoFrame. OntoFrame is service framework which includes ontology, reasoning engine, triple store. In our study, we propose the design methodology of ontology and service system for academic information based on OntoFrame. And then we explain the components of service system, processing steps of automatic recommendation of reviewers and analysis of researchers' accomplishments.