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

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.455-457
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• 2016

스마트 시대에 돌입함에 따라 사람들이 여가를 즐기는 방법은 다양해졌고 그 중에서 독서는 아직까지도 사랑받고 있는 여가 방법 중 하나이다. 그에 따라 수많은 문학도서가 출판되고 있으며 다양해 지고 있는 장르 중에서 소설의 출판량은 다른 타 장르에 비해 가히 압도적이다. 이러한 상황 속에서 사용자에게 적합한 소설을 추천하기란 어려운 일이다. 따라서 본 논문에서는 사용자의 개인 성향과 협업 필터링 방법을 이용하여 각각의 개인 성향에 적합한 소설을 추천하는 시스템을 제안한다.

• Journal of Broadcast Engineering
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• v.17 no.1
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• pp.129-139
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• 2012

This paper introduces a recommender system that is to recommend broadcast content. Our recommender system uses user's viewing history for personalized recommendations. Broadcast contents has unique characteristics as compared with books, musics and movies. There are two types of broadcast content, a series program and an episode program. The series program is comprised of several programs that deal with the same topic or story. Meanwhile, the episode program covers a variety of topics. Each program of those has different topic in general. Therefore, our recommender system recommends TV programs to users according to the type of broadcast content. The recommendations in this system are based on user's viewing history that is used to calculate content similarity between contents. Content similarity is calculated by exploiting collaborative filtering algorithm. Our recommender system uses java sparse array structure and performs memory-based processing. And then the results of processing are stored as an index structure. Our recommender system provides recommendation items through OPEN APIs that utilize the HTTP Protocol. Finally, this paper introduces the implementation of our recommender system and our web demo.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.140-142
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• 1999

본 논문에서 구현하고자 하는 에이전트기반 전자도서관 시스템은 웹을 통해 도서정보를 검색하는 사용자들에 대한 도서 정보 관심도를 모니터 에이전트에 의해 추출하게 하고 해당 정보를 대상으로 학습 과정을 거쳐 사용자별 프로파일을 구축할 수 있게 한다. 이 과정에서 모니터 에이전트에서 추출되는 사용자 history와 학습예제 정보는 사용자가 행하는 다양한 작업의 결과 차별화된 중요도가 적용된다. 학습 에이전트를 통해 구축된 사용자 관심 정보는 각 사용자에게 세부 DB 추천 모듈과 관심 정보를 푸쉬해주는 에이전트에 적용된다. 본 시스템에서는 전자 도서관에 가입하는 사용자들의 공통된 그룹 정보를 관리하여 이를 통해 동일 그루내의 사용자들 사이의 정보 이용이 가능하게 하였다. 본 논문에서는 개인과 그룹을 대상으로 하는 모니터 에이전트와 학습 에이전트를 설정하여 도서관을 이용하는 사용자들에게 정확하고 신속한 정보를 제공하는 것을 목적으로 한다.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.301-307
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• 2020

In recent years, a recommendation system is introduced in many different fields with the beginning of the 5G era and making a considerably prominent appearance mainly in books, movies, and music. In such a recommendation system, however, the preference degrees of users are subjective and uncertain, which means that it is difficult to provide accurate recommendation service. There should be huge amounts of learning data and more accurate estimation technologies in order to improve the performance of a recommendation system. Trying to solve this problem, this study proposed a movie recommendation system based on Fuzzy-AHP and Word2vec. The proposed system used Fuzzy-AHP to make objective predictions about user preference and Word2vec to classify scraped data. The performance of the system was assessed by measuring the accuracy of Word2vec outcomes based on grid search and comparing movie ratings predicted by the system with those by the audience. The results show that the optimal accuracy of cross validation was 91.4%, which means excellent performance. The differences in move ratings between the system and the audience were compared with the Fuzzy-AHP system, and it was superior at approximately 10%.

• Journal of Korean Library and Information Science Society
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• v.41 no.1
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• pp.149-165
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• 2010

In this research, we analyze about the general service provided by Society of Korean journal articles of science and technology. Personalized retrieval services which are suitable to the articles service were developed based on this. That is, there are personalized retrieval system based on user's keyword, authors navigation system, automatic topic recommendation system based on author's keyword, and similar user automatic recommendation system. In this research, personalized service methods being suitable to the articles service of Society tries to be considered through the user survey.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.280-282
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• 2001

인터넷의 발달로 웹에서 제공되는 정보와 서비스의 양이 폭발적으로 증가하고 있다. 따라서 사용자들은 보다 쉽게 빠르게 정보를 찾을 수 있도록 지원해 주는 정보 검색 에이전트에 대한 개발을 요구하고 있다. 본 논문에서는 사용자에게 적합한 도서 정보를 검색 및 추천하는 멀티 에이전트 시스템을 설계한다. 그리고 여러 에이전트 사이의 행동을 제어하는 CA(Coordination Agent) 에이전트의 조정 기능과 각 에이전트간의 통신을 지원하는 KQML처리의 알고리즘을 구현한다. 구현 결과 CA의 Message Queue 비교를 토한 조정 기능은 IEA(Information Extraction Agent)의 중복된 정보검색을 방지하여 보다 빠르고 효율적인 정보 검색의 효과를 얻을 수 있었고, KQML은 JAVA의 클래스로 구현하여 본 시스템에서 사용된 메시지 이외의 모든 KQML 메시지 처리 기능을 포함할 수 있도록 하였다.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.510-519
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• 2013

In this paper, we propose a voice-annotation browsing system that make the reading-disabled people to be able to find and play the existing voice-annotations. The proposed system consists of 4 steps: input, ranking & recommendation, search, and output. For the reading-disabled people depending only on the auditory sense, all steps can accept voice commands. To evaluate the effectiveness of our system, we design and implement an android-based mobile e-book application supporting the voice-annotation browsing ability. The implemented system is tested by a number of blind-folded users. As a result, we can see almost all the reading-disabled people can successfully and easily reach the existing voice-annotations they want to find.

• Journal of Korean Library and Information Science Society
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• v.48 no.4
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• pp.351-374
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• 2017

In this study, we propose a model for constructing a producer-centered self-publishing repository for the personal digital records and three operational process for practical application of the operational model. 1) As essential requirements for constructing a DSpace-based self-publishing repository, we propose producer-centered service provision, management subject, operation and management plan, how to activate the repository of digital personal records producers, copyright issues, development and dissemination process. 2) The repository model constructs a producer-centered circular structure considering these requirements. 3) Through a repository model with multiple agencies, it provides various services such as content distribution, keyword search, usage statistics, recommendation system, and open access to portal users and electronic publishers as well as individual users.