• 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".

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.617-624
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• 2001

With the proliferation of E-Commerce, product informations and services are provided to customers diversely. Thus customers want a software agent that can retrieve and recommend goods satisfying various purchase conditions as well as price. In this paper, we present a MAS (multi-agent system) for book information retrieval and recommendation in E-Commerce. User's preference is reflected in the MAS using the profile which is taken by user. The proposed MAS is composed of individual agents that support information retrieval, information recommendation, user interface, and web robots and a coordination agent which performs information sharing and job management between individual agents. Our goal is to design and implement this multi-agent system on a Windows NT server. Owing to the workflow management of the coordination agent, we can remove redundant information retrievals of web robots. From the results, we could provide customers various purchase conditions for several online bookstores in real-time.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.504-507
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• 2018

빅데이터가 일상이 된 현대 사회에서 책 시장의 증가와 책 양의 증가로 인하여 책을 개인에 맞게 선택하는데 어려움이 있다. 그래서 개인 맞춤 추천 시스템이 필요하다. 개인 맞춤 추천 시스템에서 가장 많이 사용하는 방법은 협업 필터링 방법이 있다. 협업 필터링은 희박성 문제를 가지고 있다. 본 논문에서는 협업 필터링 방법에 희박성 문제를 해결하기 위하여 지역, 나이, 성별, 장르 등 개인 성향을 이용하고, 기존의 책 리뷰를 오피니언 마이닝 기법을 적용하여 개인 맞춤형 도서를 추천하는 추천시스템을 제안한다.

• Journal of Korean Library and Information Science Society
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• v.48 no.2
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• pp.215-236
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• 2017

The purpose of this study is to develop an automatic classification system for recommending appropriate books of 9 enneagram personality types, using book information data reviewed by librarians. Data used for this study are book review of 501 recommended titles for children and young adults from National Library for Children and Young Adults. This study is implemented on the assumption that most people prefer different types of books, depending on their preference or personality type. Performance test for two different types of machine learning models, nonlinear kernel and linear kernel, composed of 360 clustering models with 6 different types of index term weighting and feature selections, and 10 feature selection critical mass were experimented. It is appeared that LIBLINEAR has better performance than that of LibSVM(RBF kernel). Although the performance of the developed system in this study is relatively below expectations, and the high level of difficulty in personality type base classification take into consideration, it is meaningful as a result of early stage of the experiment.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.3
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• pp.343-372
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• 2021

The purpose of this study is to select keywords that can recommend books by personality type, and to test whether the chosen keywords can be actually used in the categorization and customized recommendation of books for each personality type. To achieve the research goal, this study chose books that match the level of fifth and sixth grade elementary school students and first grade middle school students and commissioned an expert group to categorize the books into groups that are preferred by each personality type. As a result of the classification, half of the books in which more than five experts agreed showed high consensus. In addition, the results of classifying books by personality type with keywords extracted by the automatic word extraction system by collecting the book review data of the selected books were similar to the results of the final judgement by the expert group, except for a few books. In conclusion, this study proved that it is possible to classify and recommend books that are likely to be preferred by different personality types using review keywords.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.247-249
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• 2015

본 논문에서는 하둡 플랫폼의 맵리듀스 모델에 기반하여 도서관 이용자들이 자주 대출하는 도서와 키워드 매칭을 통해 연관성이 높은 도서들을 추출하고 추천해 주는 도서 대출 추천 시스템을 구현 개발한다. 구현 개발된 시스템은 빅데이터의 특징을 갖는 도서관의 대출 로그 데이터로부터 타겟 도서와 유사한 키워드를 갖고 자주 대출되는 도서를 찾아 이용자에게 제공해 준다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.201-204
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• 2010

하루에도 수많은 도서가 출판되는 현실에서 사용자가 원하는 목적에 맞는 도서를 찾아 읽기는 어려운 일이다. 본 논문에서는 방대한 분량의 도서 데이타를 바탕으로, MapReduce 모델을 활용하여 도서들 사이의 연관 관계를 추출하였다. 추출한 연관 관계 DB를 이용하여 사용자에게 서로 관련 있는 도서를 추천해줄 수 있는 시스템을 개발하고자 한다.

• Journal of Digital Contents Society
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• v.18 no.4
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• pp.707-712
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• 2017

As the amount of data increases exponentially, the recommender system is attracting interest in various industries such as movies, books, and music, and is being studied. The recommendation system aims to propose an appropriate item to the user based on the user's past preference and click stream. Typical examples include Netflix's movie recommendation system and Amazon's book recommendation system. Previous studies can be categorized into three types: collaborative filtering, content-based recommendation, and hybrid recommendation. However, existing recommendation systems have disadvantages such as sparsity, cold start, and scalability problems. To improve these shortcomings and to develop a more accurate recommendation system, we have designed a recommendation system as a factorization machine using actual online product purchase data.

• Journal of Korean Library and Information Science Society
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• v.53 no.4
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• pp.189-209
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• 2022

The purpose of this study is to analyze the content of the online book curation services and recommended books list by public libraries in Korea and to identify their properties. The case for analysis is a list of 11,447 recommended books provided by 35 online book curation services collected from 23 public libraries and the main results of the study are as follows. Only few case libraries were presenting recommendation themes, and recommendation targets were most often not specific, and the recommendation cycle of books was the most monthly. In general, books recommended for book curation do not overlap with each other, but there was overlap in the field of literature (novels) published in 2019~2021. For recommended books, the proportion of books published by some publishers was high, and books published in 2019~2021 were the most common. The subject areas analyzed based on the KDC 6th ed were literature the most. Readers analyzed by ISBN were of in the order of cultural books and children's books, and the type of publication was in the order of books, pucture books, and comics. Based on these research results, it was required to develop guidelines for online book curation service for public libraries and build a platform to share with libraries.