• Asia pacific journal of information systems
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• v.16 no.3
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• pp.159-176
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• 2006

Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8195-8209
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• 2015

The purpose of this paper is to propose a method for recommendation and personalization of important news articles based on evaluating news value. Evaluation of news is the approach by which editors select news articles for cover-story in traditional offline news papers area. For this, my study proposes a suite of methods to select and personalize a set of news based on evaluating news articles, not just on the personal preference for them. The aforementioned the value of news articles including social impact, novelty, relevance to each audience, and human interest, all of which have been factorized in many previous studies, is a main concept for a procedural and structural application methodology deduced in this study. After a comparative case study with other online news services, it was shown that my research provides more effective way to select important news articles in terms of user satisfaction than others.

• Knowledge Management Research
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• v.21 no.1
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• pp.175-195
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• 2020

The development of platform service based on the information and communication technology has revolutionized patterns of commercial transactions, driving the growth of global economy. Furthermore, the radical advancement of artificial intelligence(AI) presents the huge potential to innovate almost all the industrial and economic activities. Given these technological developments, the goal of this paper is to investigate AI's impact on the platform service innovation as well as its influence on the business performance. For the goal, this paper presents the review of the types of service innovation, the nature of platform services, and technological characteristics of leading AI technologies, such as chatbot and recommendation system. As an empirical study, this paper performs a multiple case study of Kakao Group which is the leading mobile platform service with the most advanced AI in Korea. To understand the role and effect of AI on Kakao platform service, this study investigated three cases, including chatbot agent of Kakao Bank, Smart Call service of Kakao Taxi, and music recommendation system of Kakao Mellon. The analysis results of the case study show that AI initiated innovations in platform service concepts, service delivery, and customer interface, all of which lead to a significant decrease in the transaction costs and the personalization of services. Finally, for the successful development of AI, this research emphasizes the significance of the accumulation of customer and operational data, the AI human capital, and the design of R&D organization.

• Journal of Information Technology and Architecture
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• v.10 no.2
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• pp.251-261
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• 2013

The appearance of various mobile Internet environment access to existing networks of mobile devices is easier to tell. In addition, mobile device users to use the wireless environment than a wired environment, user profile information is readily available features. Mobile devices have features that use alone. These characteristics of mobile devices to apply the personalization service is the best system. This paper proposes for mobile device users a personalized mobile music content recommendation service. This service propose to utilizes the user's access history information using time-weighting and collaborative filtering. Access history information can find out information of user interest. Using this information, consider the preference of music genre and time-weighted based on the recommendations makes the music. This method the problem of the traditional music recommendation system, point user's favorite music genre is changing over time do not consider that to solve the problem.

• Journal of the Korean Society for information Management
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• v.20 no.4 s.50
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• pp.113-134
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• 2003

With SDI service provided in libraries and information centers traditionally, this paper studies component technologies and structure of system platform in PIS(personalization information service based on the customized information service served currently in some institutions. The PIS system should provide relevant information as an output through the learning system analyzing user information searching behavior as an input value with personal profile information. To do it, this paper studies requirements and algorithms to develop PIS, and proposes learning system and recommendation system as core components in PIS.

• Convergence Security Journal
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• v.8 no.4
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• pp.167-179
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• 2008

As ubiquitous technology develops, there are many studies to provide various contents proper to users through a mobile device. However, there is a limit of information provision due to a small user interface of a mobile device. This study proposes a system that can solve a problem and provide an intelligent agent model appropriate to a mobile environment and job information positively that an individual user is interested. It is composed of a personalization engine to monitor users' behavior patterns and a learning algorithm to provide information to a mobile device. Analysis shows that preferred job items are different by sex, age and education, while a region affects job searching significantly.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.89-104
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• 2007

This research attempts to give a personalized recommendation framework in large-sized music contents environment. Despite of existing studios and commercial contents for recommendation systems, large online shopping malls are still looking for a recommendation system that can serve personalized recommendation and handle large data in real-time. This research utilizes data mining technologies and new pattern matching algorithm. A clustering technique is used to get dynamic user segmentations using user preference to contents categories. Then a sequential pattern mining technique is used to extract contents access patterns in the user segmentations. And the recommendation is given by our recommendation algorithm using user contents preference history and contents access patterns of the segment. In the framework, preprocessing and data transformation and transition are implemented on DBMS. The proposed system is implemented to show that the framework is feasible. In the experiment using real-world large data, personalized recommendation is given in almost real-time and shows acceptable correctness.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.476-479
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• 2005

The information available on the Internet has become widely used, primarily due to the ability of Web based E-Commerce and M-Commerce Retrieval Engines to find useful information for users. However, present day Commerce Retrieval Engines are far from perfect because they return results based on simple user keyword matches without any regard for the concepts in which the user is interested. In this thesis, we design and evaluate a Recommender system for web context aware based information retrieval using user profiles. Also, we designed personalization framework in ubiquitous environment based both e-commerce and m-commerce and presented the interaction of user profile including User XQuery pattern in semantic web.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2531-2533
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• 2003

네트워크의 발달과 고성능 PC의 보급이 증가함에 따라 웹을 통한 이메일 사용량도 기하급수적으로 많아지고 있다. 또한 일반 사용자나 e-Commerce상에서 오가는 메일의 양도 갈수록 늘어나고 있다. 편리하다는 점을 이용해서 엄청난 양의 스팸메일도 매일 같이 쏟아져 나와 사회적 문제점으로 부각되고 있는 현실이다. 본 연구에서는 이메일 사용자 개개인에 맞게 메일을 자동 관리해주는 웹 메일 개인화를 위한 에이전트(An Agent for Web Mail Personalization)를 제안하고자 한다. 사용자가 새로운 메일을 받게 되면 먼저 사용자의 메일 처리과정을 학습하고, 각각 개인에 맞는 룰을 형성하고, 만들어진 개인적 룰(Personal Rule)를 바탕으로 메일을 자동 관리한다. 제안된 에이전트는 카테고리 설정, 카테고리별 분류 및 저장, 불필요한 메일이나 스팸메일을 자동 삭제 해 주는 것이다. 또한 자동분류 외에 수신된 메일에 대한 추천 카테고리(Recommendation Category)를 사용자에게 고려하게 하는 기능도 추가하였다.

• Asia pacific journal of information systems
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• v.27 no.2
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• pp.126-138
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• 2017

The proliferation of items increased the difficulty of customers in finding the specific items they want to purchase. To solve this problem, companies adopted recommender systems, such as collaborative filtering systems, to provide personalization services. However, companies use only meaningful and essential data given the explosive growth of data. Some customers are concerned that their private information may be exposed because CF systems necessarily deal with personal information. Based on these concerns, we analyze the effects of the amount of information on recommendation performance. We assume that a customer could choose to provide overall information or partial information. Experimental results indicate that customers who provided overall information generally demonstrated high performance, but differences exist according to the characteristics of products. Our study can provide companies with insights concerning the efficient utilization of data.