• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2743-2762
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• 2014

Promoted by cloud technology and new websites, plenty and variety of Web services are emerging in the Internet. Meanwhile some Web services become outdated even obsolete due to new versions, and a normal phenomenon is that some services work well only with other services of older versions. These laggard or improper services are lowering the performance of the composite service they involved in. In addition, using current technology to identify proper semantic services for a composite service is time-consuming and inaccurate. Thus, we proposed a clustering method and a recommendation method to deal with these problems. Clustering technology is used to classify semantic services according to their topics, functionality and other aspects from plenty of services. Recommendation technology is used to predict the possible preference of a composite service, and recommend possible component services to the composite service according to the history information of invocations and similar composite services. The experiments show that our clustering method with the help of Ontology and TF/IDF technology is more accurate than others, and our recommendation method has less average error than others in the series of missing rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1097-1104
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• 2011

In this paper, we propose a semantic web approach for personalized book recommendation. Our approach takes advantage of the content-based recommendation and improves its disadvantage that users should input their interesting fields into all book search systems they use. Our approach provides the sharing of users' profile with their interesting fields by enabling user's interesting fields to be described over each book classification ontology of various book information providers. We also provide a middleware that manages users' profiles written in RDF and analizes similarity between user's interesting field and each concept over the book classification ontology. Our approach provide better performance than traditional keyword-based search by sharing the user's profile among book recommendation systems.

• Journal of the Ergonomics Society of Korea
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• v.23 no.3
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• pp.1-12
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• 2004

In recent years, with the advent of e-Commerce the need for personalized services and one-to-one marketing has been emphasized. To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. In this paper, we provide an intelligent agent approach to incorporate human sensibility into an one-to-one recommendation service in cyber shopping mall. Our system exploits human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system`s behavior requires the parallel execution of several tasks during the interaction （e. g., identifying the customer`s emotional preference and dynamically generating the pages of the store catalog）. The recommendation agent system composed of five modules including specialized agents carries on these tasks. By presenting goods that are consistent with user interests as well as user sensibility, the accuracy and satisfaction of the recommendation service may be improved.

• Management Science and Financial Engineering
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• v.16 no.2
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• pp.29-38
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• 2010

As the Internet is more embedded in people's lives, Internet users draw on new Internet applications to express themselves through "user-created content (UCC)." In addition, there is a noticeable shift from text-centered contents mainly posted on bulletin boards to multimedia contents such as images and videos on UCC web sites. The changes require different way of recommendations comparing to traditional products or contents recommendation on the Internet. This paper aims to design UCC recommendation methods with user behavior data and contents metadata such as tags and titles, and compare performances of the suggested methods. Real web logs data of a major Korean video UCC site was used to empirical experiments. The results of the experiments show that collaborative filtering technique based on similarity of UCC customers' preferences performs better than other content-based recommendation methods based on tag information and content metadata.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.471-476
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• 2004

Recently, provides information which is most necessary to the user the research against the web information recommendation system for the Internet shopping mall is actively being advanced. the back which it will drive in the object. In that Dynamic Web Recommendation Method Using SOM (Self-Organizing Feature Maps) has the advantages of speedy execution and simplicity but has the weak points such as the lack of explanation on models and fired weight values for each node of the output layer on the established model. The method proposed in this study solves the lack of explanation using the Bayesian reasoning method. It does not give fixed weight values for each node of the output layer. Instead, the distribution includes weight using Hybrid SOM. This study designs and implements Dynamic Web Recommendation Method Using Hybrid SOM. The result of the existing Web Information recommendation methods has proved that this study's method is an excellent solution.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.61-77
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• 2003

Due to the wide expansion of the internet, people can freely get information what they want with lesser efforts. However without adequate forms or rules to follow, it is getting more and more difficult to get necessary information. Because of seemingly chaotic status of the current web environment, it is sometimes called "Dizzy web" The user should wander from page to page to get necessary information. Therefore we need to construct system which properly recommends appropriate information for general user. The representative research field for this system is called Recommendation System(RS), The collaborative recommendation system is one of the RS. It was known to perform better than the other systems. When we perform the web user modeling or other web-mining tasks, the continuous feedback data is very important and frequently used. In this paper, we propose a collaborative recommendation system which can deal with the continuous feedback data and tried to construct the web page prediction system. We use a sojourn time of a user as continuous feedback data and combine the traditional model-based algorithm framework with the Support Vector Regression technique. In our experiments, we show the accuracy of our system and the computing time of page prediction compared with Pearson's correlation algorithm.algorithm.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.59-72
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• 2004

The range of Internet usage is drastically broadened and diversed from information retrieval and collection to many different functions. Contrasting to the increase of Internet use, the efficiency of finding necessary information is decreased. Therefore, the need of information system which provides customized information is emerged. Our research proposes Web Service based recommendation system which employes inference engine to find and recommend the most appropriate products for users. Web applications in present provide useful information for users while they still carry the problem of overcoming different platforms and distributed computing environment. The need of standardized and systematic approach is necessary for easier communication and coherent system development through heterogeneous environments. Web Service is programming language independent and improves interoperability by describing, deploying, and executing modularized applications through network. The paper focuses on developing Web Service based recommendation system which will provide benchmarks of Web Service realization. It is done by integrating inference engine where the dynamics of information and user preferences are taken into account.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.287-296
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• 2002

웹 사용 마이닝은 데이터마이닝을 바탕으로 사용자의 로그 파일 정보를 이용하여 웹이 이용되는 패턴을 발견한다. 이를 이용하여 웹을 개선하여 사용자들이 보다 빨리 원하는 내용을 검색할 수 있도록 할 수 있으며 시스템 관리자에게는 효율적인 웹 구조를 인한 정보를 제공할 수 있다. 웹 사용 마이닝에서 사용하는 데이터는 성형화되어 있지 않으며 웹 사용 패턴을 분석하는데 방해가 되는 잡음 데이터까지 포함하고 있다. 이것은 기존에 개발된 여러 데이터마이닝 기법을 적용하는데 어려움으로 작용한다. 이러한 어려움을 해결하기 위해 본 논문에서는 새로운 방법을 도입한 SPMiner을 .제안한다. SPMiner는 웹의 구조를 이용하여 로그 파일의 전처리 과정을 줄이며 사용자의 탐색 패턴 분석을 효율적으로 수행 할 수 있는 시스템이다. SPMiner는 WebTree 에이전트를 이용하여 웹 사이트 구조를 분석하여 WebTree를 생성하고 사용자 로그 파일을 분석하여 각 웹 페이지의 사용빈도에 대한 정보를 추출한다. WebTree와 로그 파일에서 추출된 웹 페이지에 대한 정보는 SPMiner에 의해 패턴을 분석할 퍼 이용될 수 있는 형태인 WebTree$^{+}$로 병합된다 WebTree$^{+}$는 패턴 발견을 쉽게 해주며 사용자에게 추천할 정보나 웹 페이지를 능동적으로 추천할 수 있게 만들어 준다.

• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.318-327
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• 2008

With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.171-180
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• 2003

The recent paradigm of web-based teaching-learning is changing into a direction that analyzes the learning patterns of learners on the basis of learners' ability, aptitude, request, interest, learning history, activity profile, etc. and provides adaptive environment with individual learners The present study analyzed learners' learning patterns using data on learning activities and developed a personalization system that provides learning environment adapted to individual learners. This study customized in three aspects, which are recommendation of learning path, recommendation of interface and recommendation of interaction, through Web mining. The personalization system developed in this study was proved to be effective in improving individual learners' satisfaction with learning in Web-based teaching-learning environment.