• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1878-1890
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• 2016

Web 2.0 has features produced the content through the user of the participation and share. The content production activities have became active since social network service appear. The social bookmark, one of social network service, is service that lets users to store useful content and share bookmarked contents between personal users. Unlike Internet search engines such as Google and Naver, the content stored on social bookmark is searched based on tag keyword information and unnecessary information can be excluded. Social bookmark can make users access to selected content. However, quick access to content that users want is difficult job because of the user of the participation and share. Our paper suggests a method recommending search word to be able to access quickly to content. A method is suggested by using Collaborative Filtering and Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare by 'Delicious' and "Feeltering' with our system.

• KSCI Review
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• v.14 no.2
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• pp.135-139
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• 2006

There have been many suggestions of ontology-based semantic web service systems and recommendation systems, but there has been no attempt to study educational consultation. Thus this study set out to tackle the problem and design specialized service to reflect individuals' interest, aptitude, hobbies, and grades based on the semantic web technology so that they could search the related information easily by managing personal profiles and identifying the meanings between information. It also aimed to suggest a convenient information service system of personalized educational consultation.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.509-517
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• 2023

The reactive web service provides a related word recommendation system using the TextRank algorithm and a word-based idea generation service selected by the user. In the related word recommendation system, the method of weighting each word using the TextRank algorithm and the probability output method using SoftMax are discussed. The idea generation service discusses the idea generation method and the artificial intelligence reinforce-learning method using mini-GPT. The reactive web discusses the linkage process between React, Spring Boot, and Flask, and describes the overall operation method. When the user enters the desired topic, it provides the associated word. The user constructs a mind map by selecting a related word or adding a desired word. When a user selects a word to combine from a constructed mind-map, it provides newly generated ideas and related patents. This web service can share generated ideas with other users, and improves artificial intelligence by receiving user feedback as a horoscope.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.34-36
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• 2022

Over the last decades, information has increased exponentially due to SNS(Social Network Service), IoT devices, World Wide Web, and many others. Therefore, it was monumentally hard to offer a good service or set of recommendations to consumers. To surmount this obstacle numerous research has been conducted in the Data Mining field. Different and new recommendation models have emerged. In this paper, we proposed a Content-based movie recommendation system using demographic information of users and the average rating for genres. We used MovieLens Dataset to proceed with our experiment.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1013-1023
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• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.3
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• pp.31-38
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• 2013

The evolution of the Web from Web 1.0 to Web 2.0 has brought up new platforms as SNSs(Social Network Service) that are used by users to articulate and manage their relationships. SNSs are an online phenomenon which has become extremely popular. A SNS essentially consists of a representation of each user, his/her social links, and a variety of additional services. SNSs are increasingly attracting the attention of academic and industry researchers. What makes SNS unique is that they have a relationship with friends. The friend recommendation is one important feature of social networking services. People tend to trust the opinions of friends they know rather than the opinions of strangers. In this paper, we propose an efficient data processing method for social data. We study previous researches about social score in social network service. Our ESS(Efficient Social Score) is computed by both friendship weight and score of a document that was tagged by a user's friends. Our experimental results also confirm that our method has good performance.

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

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.257-264
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• 2012

The development of mobile devices and the spread of wireless network help share and exchange information and resources more easily. The bond them to Cloud Computing technology help pay attention to "Mobile Cloud" service, so there have been being a lot of studies on "Mobile Cloud" service. Especially, the important of 'Recommendation Service' which is customized for each user's preference and context has been increasing. In order to provide appropriate recommendation services, it enables to recognize user's current state, analyze the user's profile like user's tendency and preference, and draw the service answering the user's request. Most existing frameworks, however, are not very suitable for mobile devices because they were proposed on the web-based. And other context information except location information among user's context information are not much considered. Therefore, this paper proposed the context-aware framework, which provides more suitable services by using user's context and profile.

• Journal of Information Management
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• v.40 no.1
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• pp.199-215
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• 2009

The web 2.0 concept rapidly spreads to the various field which is based on an opening, the participation, and a share. And the research about the recommendation system, that is the personalize feature, and social network is very active. In the case of the recommendation system and social network, it had been developing in the respectively different area and the new research toward the service model of a form that it fuses these is insignificant. In this paper, I'm going to introduce efficient social network which is called the researcher connection network. It is possible to recommend the researcher intellectually who studies the similar field by analyzing the usage log and user profile. Through this study, we could solved the network expandability problem which is due to the user passive participation and the difficulty of the initial network construction that is the conventional social network problem.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.1-10
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• 2015

We propose and implement a personalized tour recommendation system based on ontology. We utilize user's profile, dynamic information on search in the application, web search, and facebook for personalized recommendation. We construct tour database for England based on ontology for a demo service, and recommend tour spot considering an individual preference with tour database. This dynamic and personalized tour service makes it possible for individual to plan one's own tour by considering recommended tour spots for each individual.