• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.565-570
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• 2007

네트워크의 발달은 유선 인터넷(Wired LAN)과 무선 인터넷(Wireless LAN) 시대를 지나 휴대 인터넷(Mobile LAN)으로 발전하고 있다. 이처럼 다양한 네트워크의 공존은 사용자에게 보다 빠르고 저렴한 서비스를 제공하고 있다. 본 논문에서는 모바일 기기 사용자를 위한 개인화 방법으로 협업 필터링 방법을 통한 추천과 푸쉬(push) 방식의 서비스 방법을 제안한다. 사용자 프로파일 정보는 협업 필터링 방법을 통한 사용자 선호 음악 추천을 수행하고, 추천된 사용자 선호 음악은 모바일 기기로 푸쉬 서비스 된다. 추천을 통한 모바일 음악 푸쉬 서비스는 모바일 기기 사용자로 하여금 네트워크 환경에 접속되어있을 때 사용자 취향에 맞는 음악을 능동적으로 다운로드 해 둠으로써 사용자가 음악을 선택하여 모바일 기기로 다운로드 하는 시간을 줄여 줄 수 있다.

• The Journal of Information Technology and Database
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• v.8 no.2
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• pp.1-15
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• 2001

In trying to bring about online CRM(customer relationship management), companies have paid much attention to eCRM. The key of eCRM is a recommendation system, which is being used by E-commerce sites to find products to purchase. To maintain a constant flow of marketing information and feedback it is important to staying in touch with customers. In this respect, eCRM becomes a serious business tool for sales activities. In this article we present tee case of Kookmin Internet banking eCRM welch is one of the first examples of implementing eCRM in commercial web site in Korea. We examine how Kookmin Internet banking develops eCRM and how it provides customized services to customers. We also explore the role of eCRM in Internet banking and the level of personalization technology used in Kookmin eCRM case.

• Knowledge Management Research
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• v.21 no.2
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• pp.177-196
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• 2020

Since the adoption of K-IFRS(Korean International Financial Reporting Standards), the amount of financial footnotes has been increased. However, due to the stereotypical phrase and the lack of conciseness, deriving the core information from footnotes is not really easy yet. To propose a solution for this problem, this study tried financial footnote analysis for financial ratio predictions based on text-mining techniques. Using the financial statements data from 2013 to 2018, we tried to predict the earning per share (EPS) of the following quarter. We found that measured prediction errors were significantly reduced when text-mined footnotes data were jointly used. We believe this result came from the fact that discretionary financial figures, which were hardly predicted with quantitative financial data, were more correlated with footnotes texts.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.331-339
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• 2004

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this paper, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.56-67
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• 2019

The aim of this study is to develop Artificial Intelligence (AI) based business models of media firms. We define AI and discuss 'AI activity model'. The practices of the efficiency model are home equipment-based personalization and media content recommendation. The practices of the expert model are media content commissioning, content rights negotiation, copyright infringement, and promotion. The practices of the effectiveness model are photo & video auto-tagging and auto subtitling & simultaneous translation. The practices of the innovation model are content script creation and metadata management. The related use cases from 2012 to 2017 are introduced along the four activity models of AI. In conclusion, we propose for media companies to fully utilize the AI for transforming from traditional to successful digital media firms.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.23-31
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• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.1
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• pp.38-46
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• 2009

With the rapid increase in the number of Web documents, the problem of information overload is growing more serious in Internet search. In order to ease the problem, researchers are paying attention to personalization, which creates Web environment fittingly for users' preference, but most of search engines produce results focused on users' queries. Thus, the present study examined the method of producing search results personalized based on a user's understanding level. A characteristic that differentiates this study from previous researches is that it considers users' understanding level and searches documents of difficulty fit for the level first. The difficulty level of a document is adjusted based on the understanding level of users who access the document, and a user's understanding level is updated periodically based on the difficulty of documents accessed by the user. A Web search system based on the results of this study is expected to bring very useful results to Web users of various age groups.

• The Journal of Korean Association of Computer Education
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• v.13 no.1
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• pp.45-52
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• 2010

With rapid increase in the number of web documents, the problem of information overload in Internet search is growing seriously. In order to improve web search results, previous research studies employed user queries/preferred words and the number of links in the web documents. In this study, performance of the search results exploiting these two criteria is examined and other preference criteria for web documents are analyzed. Experimental results show that personalized web search results employing queries and preferred words yield up to 1.7 times better performance over the current search engine and that the search results using the number of links gives up to 1.3 times better performance. Although it is found that the first of the user's preference criteria for web documents is the contents of the document, readability and images in the document are also given a large weight. Therefore, performance of web search personalization algorithms will be greatly improved if they incorporate objective data reflecting each user's characteristics in addition to the number of queries and preferred words.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.117-135
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• 2003

This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity.

• The Journal of the Korea Contents Association
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• v.15 no.8
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• pp.481-494
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• 2015

Recently mobile application providers and telecommunication companies went through a difficult time in a highly competitive mobile and its application market where we've seen a huge trend for diverse mobile applications occurring on smart phone. If there were a time when those of companies need to analyze factors affecting users' intention to download or recommend others applications more than ever, it is now. Based on UTAUT model, this research is to provide them with strategic implications by analyzing those factors according to application types with utilization and hedonic values. As a result, firstly trust and personalization have positive impact on Performance Expectancy and users' intention to use have been significantly affected by Performance Expectancy and Effort Expectancy. Secondly the result of path analysis has a different outcome according to application types with utilization and hedonic values. Therefore it is expected that the research gives practical and strategic implication for application developer, mobile companies and others helping application development, new service launch and marketing implementation.