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

• The Korean Publising Journal, Monthly
• /
• s.334
• /
• pp.28-29
• /
• 2003

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

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.2
• /
• pp.121-129
• /
• 2015

With the explosive growth of information about books, there is a growing number of customers who find it difficult to pick a book. Against the backdrop, the importance of a book recommendation system becomes greater, through which appropriate information about books could be offered then to encourage customers to buy a book in the end. However, existing recommendation systems based on the bibliographical information or user data reveal the reliability issue found in their recommendation results. This is why it is necessary to reflect semantic information extracted from the texts of a book's main body in a recommendation system. Accordingly, this paper suggests a method for extracting keywords from the main body of novels, as a preceding research, by using TF-IDF method as well as the text structure. To this end, the texts of 100 novels have been collected then to divide them into four structural elements of preface, dialogue, non-dialogue and closing. Then, the TF-IDF weight of each keyword has been calculated. The calculation results show that the extraction accuracy of keywords improves by 42.1% in performance when more weight is given to dialogue while including preface and closing instead of using just the main body.

• Journal of Korean Library and Information Science Society
• /
• v.52 no.2
• /
• pp.253-274
• /
• 2021

With the introduction of 'Reading One Book a Semester' in the 2015 curriculum, more and more activities are being done to read the same books on a classroom basis. Accordingly, librarians need to properly support the teacher's book recommendation and book selection process, which consists of classes. This study aims to establish effective school library services based on this by identifying differences in elementary school students' reading interests when reading a book recommended by a teacher in the class. To this end, all students of A Elementary School were given a book selection class by class to select one of the teacher's recommended books, and students were interested in reading before and after reading. As a result, students who read the books they chose showed higher 'interest after reading' than those who read the books they did not choose. However, students who had high 'interest before reading' even though they read books they did not choose showed high 'interest after reading'. 'Interest before reading' were higher for students who were provided with book information evenly and used the school library more frequently. Therefore, librarians need to provide a variety of book information and services to increase the frequency of school library use to increase 'interest before reading', which positively affects self-selective reading.

• Journal of the Korean Society for information Management
• /
• v.39 no.3
• /
• pp.311-336
• /
• 2022

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users' book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories('Content', 'Character', 'Writing', 'Reading', 'Author', 'Story', 'Form') were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

• The Korean Publising Journal, Monthly
• /
• s.336
• /
• pp.136-136
• /
• 2003

• 건축구조
• /
• v.9 no.3
• /
• pp.75-75
• /
• 2002

• The Korean Publising Journal, Monthly
• /
• s.15
• /
• pp.4-5
• /
• 1988

• 건축구조
• /
• v.12 no.1
• /
• pp.99-99
• /
• 2005