• The Journal of Society for e-Business Studies
• /
• v.13 no.4
• /
• pp.173-191
• /
• 2008

As volumes of electronic commerce increase rapidly, customers are faced with information overload, and it becomes difficult for them to find necessary information and select what they need. In this situation, recommender systems can help the customers search and select the products and services they need more conveniently. These days, the recommender systems play important roles in customer relationship management. In this research, we develop a recommender system that recommends the books to the customers of Internet bookstore. In previous researches on recommender systems, collaborative filtering technique has been often employed. For the collaborative filtering technique to be used, the rating scores on books given by previous purchasers have to be collected. However, the collection of rating scores is not an easy task in reality. Therefore, in this research, we employed case-based reasoning technique that can work only with the book purchase history of customers. The accuracy of recommendation of the resulting book recommender system was about 40% on the level 3 classification code.

• Journal of Korea Multimedia Society
• /
• v.16 no.4
• /
• pp.510-519
• /
• 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 the Korean Institute of Intelligent Systems
• /
• v.12 no.2
• /
• pp.109-114
• /
• 2002

E-commerce customers can reduce purchasing cost by the help of comparison shopping agents that collect price information of products in the shopping malls. However, user expects a software agent that can recommend product information satisfying various purchase conditions besides price. In this paper, we present a MAS (multi-agent system) which retrieves and recommends book information suitable for various user needs to realize an agent-based E-Commerce. We implemented and tested our MAS to help on-line bookstore customers. From the results, we could provide E-commerce customers various book purchase conditions for several online bookstores in real-time.

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

• Journal of Korean Library and Information Science Society
• /
• v.52 no.1
• /
• pp.155-178
• /
• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

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

• The Journal of the Korea Contents Association
• /
• v.12 no.5
• /
• pp.52-61
• /
• 2012

Text visualization is one of sectors in data visualization. This study is on methods to visually represent text's contents, structure, and form aspects based on various analytic techniques about wide range of text data. In this study -as a text visualization study-, 1) a method to find out the characteristics of a book's genre using words in the text of the book was looked into, 2) elements of visualization of a book's genre based on verification through an experiment were drew, and 3) the ways to intuitionally and efficiently visualize this were explained. According to visualization suggested by this study, first, actual genre of a book can be understood based on words used in the book. Second, with which genre is closed to the book can be found out with one glance through images of visualization. Moreover, the characteristics of complicated genres included in a book can be understood. Furthermore, the level of closeness (similarity) of a genre -which is found to be a representative genre using the number of dots, curvature of a curve, and brightness in the image- can be assumed. Finally, the outcome of this study can be used for a variety of fields including book customizing service such as a book recommendation system that provides images of personal preference books or genres through application of books favored by individual customers.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.139-152
• /
• 2011

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid-1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors "Recommender system", "Recommendation system", "Personalization system", "Collaborative filtering" and "Contents filtering". The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master's and doctoral dissertations, textbook, unpublished working papers, non-English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k-nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.

• Journal of Internet Computing and Services
• /
• v.12 no.2
• /
• pp.85-101
• /
• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.

• Proceedings of the KIEE Conference
• /
• 1987.07b
• /
• pp.943-946
• /
• 1987

This paper describes the test techniques that are used for the implementation test of signalling link function in the signalling system No.7 recommended by 0703 in CCITT red book. The test scenarios are produced by means of the state transition diagrams which are based on the specification of CCITT recommendation 0703. This test scenarios consist of five independent tasks written in MC68000 assembly language and are scheduled to be executed in multitasking kernel. This test scenarios can also be used to test signalling link function implemented in the different environment.