• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.346-354
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• 2009

This paper proposes a new learning recommendation system for learning patterns that educators are applying to learners using item-based method. The proposed method in this paper first collects personal learning methods based on learning information that learners are performing through the internet contents site. Then this system recommends a learning method which is estimated most properly to learners after classifying learning elements based on these information. The students of a middle school took part in the experiment in order to evaluate the proposed system, and the students were divided into three groups according to their grades. We gave inter-attribute and intra-attribute weights to learning elements applying to each group for recommending the most efficient method to improve learning achievement. The experiment showed that the learning achievement of learners in the proposed method is improved considerably compared to the previous grades.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.133-141
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• 2008

Social Web is turning current Web into social platform for knowing people and sharing information. This paper takes major social tagging systems as examples, namely delicious, flickr and youtube, to analyze the social phenomena in the Social Web in order to identify the way of mediating and linking social data. A simple Tag Ontology (TO) is proposed to integrate different social tagging data and mediate and link with other related social metadata. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tag ontology is also suggested as an applying field.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.652-654
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• 2003

무선 단말기 보급의 증가로 PDA 또는 휴대폰 등을 통한 인터넷 접속이 증가하고 있다. 하지만 현재 대부분의 웹 사이트는 페이지 크기나 용량이 데스크톱을 위해 최적화 되어 있기 때문에. 무선 단말기를 이용한 원활한 인터넷 서핑이 어렵다. 이런 문제에 대한 해결책으로 개인화된 북마크를 통해 원하는 서비스에 바로 접근할 수 있는 방법을 제안한다. 본 논문에서 제안한 기법은 기존 페이지 중심의 북마크를 개선하여 웹 사이트 별로 일련의 페이지들을 북마크하여 무선 단말기를 통해 제공함으로써, 웹 사이트의 구조를 한눈에 쉽게 파악하고 원하는 페이지에 더욱 빠르게 접근할 수 있는 방안을 제시한다. 또한 사용자들의 선호도 프로파일을 이용하여 유사한 관심분야를 갖는 사용자들을 그룹화한 후 같은 그룹내의 사람들이 생성한 북마크를 웹 사이트 접근시 추천해주는 기법을 제안한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.336-339
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• 2006

추천시스템에서 가장 많이 사용하고 있는 협력적 필터링 방법을 모바일 기기 등에서 사용하려면 추천 정보와 사용자들의 평가 정보가 부족하여 추천의 질이 떨어지게 된다. 이러한 문제를 해결하기 위해 실시간으로 얻어진 컨텍스트 정보를 정량화하여 협력적 필터링에 적용함으로써 보다 나은 추천 결과를 얻을 수 있었다. 그럼에도 불구하고 평가를 하기 위한 컨텍스트 정보가 충분하지 못한 경우 부정확한 결과를 가져올 수 있다. 또한 사용자 정보 평가 과정 중 정량화 단계의 분류 과정을 단순히 하게 되면 서비스 받는 사용자가 정확한 그룹에 분류되어 정확도가 결여되는 문제가 발생한다. 본 논문에서는 실시간으로 얻을 수 있는 컨텍스트 정보가 부족한 경우, 내용 기반 필터링에서 많이 사용하고 있는 사용자 프로파일 정보를 실시간 컨텍스트 정보와 결합한다. 그리고 정량화 단계를 개선하여 협력적 필터링함으로써 기존의 방법보다 향상된 결과를 얻을 수 있다.

• 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.

• The Journal of Korean Association of Computer Education
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• v.14 no.6
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• pp.53-64
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• 2011

This paper proposes and evaluates empirically a new recommendation method in order to improve the learning achievement of learners using case-based method. In this paper, we first carried out a survey targeting teachers who work currently in Gyeongbuk area, and constructed learning cases depending on critical factors of learning. We next recommended differentiated learning methods to learners classifying according to learning cases by achievement level through this survey. The students of a middle school took part in the experiment in order to evaluate empirically the proposed learning cases. The students were divided into three groups by their achievement level and three separate learning cases were applied to each group. The weights among learning improvement elements applying to each group were added through the survey result of teachers. The experiment using the proposed case-based recommendation method showed that the learning achievement of learners is improved considerably compared to the previous one.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.79-98
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• 2011

This paper proposes a semantic recommendation technique for a personalized e-commerce portal. Semantic recommendation is achieved by utilizing the attributes of products. The semantic similarity of the products is merged with the rating information of the products to provide an accurate recommendation. The recommendation technique also analyzes various attitudes of the customer to evaluate the implicit rating of products. Attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information." We implicitly track customer attitude to estimate the rating of products for recommending products. Also we implement a session validation process to identify the valid sessions that are highly important for giving an accurate recommendation. Our recommendation technique shows a high degree of accuracy as we use age groupings of customers with similar preferences. The experimental section shows that our proposed recommendation method outperforms well known collaborative filtering methods not only for the existing customer, but also for the new user with no previous purchase record.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.345-352
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• 2013

The demand and production for mobile music increases as the number of smart phone users increase. Thus, the standard of selection of a user's preferred music has gotten more diverse and complicated as the range of popular music has gotten wider. Research to find intelligent techniques to ingeniously recommend music on user preferences under mobile environment is actively being conducted. However, existing music recommendation systems do not consider and reflect users' preferences due to recommendations simply employing users' listening log. This paper suggests a personalized music-recommending system that well reflects users' preferences. Using AHP, it is possible to identify the musical preferences of every user. The user feedback based on the Bayesian network was applied to reflect continuous user's preference. The experiment was carried out among 12 participants (four groups with three persons for each group), resulting in a 87.5% satisfaction level.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.135-146
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• 2010

In this paper, we propose an Recommendation System for supporting self-directed learning on e-learning marketplace. The key idea of this system is recommendation system using revised collaborative filtering to support marketplace. Exisiting collaborative filtering method consists of 3 stages as preparing low data, building familiar customer group by selecting nearest neighbor, creating recommendation list. This study designs recommendation system to support self-directed learning by using collaborative filtering added nearest neighbor learning course that considered industry and learning level. This service helps to select right learning course to learner in industry. Recommendation System can be built by many method and to recommend the service content including explicit properties using revised collaborative filtering method can solve limitations in existing content recommendation.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1453-1462
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• 2010

In this paper, a new program recommendation system is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with collaborative filtering method. For a user profile which describes user program preference, a program preference, sub-genre preference, and US(user similarity) weight of the user neighborhood is averaged and updated every week. In order to evaluate system performance, real 24-weeks cable TV watching data provided by Nilson Research Corp. are modified to fit for IPTV broadcasting environment and the simulation result shows quite comparative quality of recommendation. The experimental results optimum performance when user similarity based weighting, five person per group and five recommendation programs are used.