• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.494-497
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• 2010

상황기반 정보제공 에이전트는 주위의 상황을 인식하고 상황에 적합한 정보를 능동적으로 제공해주는 서비스 또는 애플리케이션 프로그램을 말한다. 상황기반 정보제공 에이전트는 현재의 상황을 정확하게 인식하고 그 상황에 적합한 정보를 추천해야 하기 때문에 상황인지 기능, 상황인식 기능, 정보검색 및 추천 기능 등 다양한 기능이 요구된다. 본 논문에서는 프로야구에서 상황기반 정보제공 서비스 실현을 위해 상황기반 관전포인트 제공 시스템을 구현한 사례를 소개하였고, 구현에 있어 시간에 따른 상황변화를 정의할 수 있는 지식화 모델 구조를 제시하였다. 그 결과 이전 프로야구에서 제공되고 있는 일방적이고, 제한적인 관전포인트 제공 서비스를 개선할 수 있었다.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1347-1356
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• 2017

In order to strengthen the competitiveness of small business startups with insufficient internal and external resources, the government has implemented SMEs consulting support projects. However, as an intangible knowledge service, consulting services also have their own shortcomings such as the risk of time, effort and quality uncertainty. And all kinds of risks will be borne by the companies which receive consulting services. Therefore, many companies which receive consulting services in Korea are questioning the reliability of the consulting results and have a negative attitude on this industry. In addition, they consider the cost-effectiveness of consulting is relatively low. Finally, this study performed an empirical study on recommendation intention of small business startups which received consulting and also focused on the comparison between Korean and Chinese startups.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.316-318
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• 2019

Recently, healthy life has become an issue in an aging society, and the number of people who have been interested in continuous health care for better life is increasing. In this paper, we implemented a personalized recommendation systm to provide convenient healthcare management for user. The PHR (Personal Health Record) of user could be stored in the server along with health related information such as lifestyle, disease, and physical condition. The users could be classified into similar clusters according to the PHR profile in order to provide healthcare contents to the users who had similar PHR profile. K-Means clustering was applied to generate clusters based on PHR profile and ACDT(Ant Colony Decision Tree) algorithm was used to provide personalised recommendation of health information stored in knowledge base. The app system developed in this paper is useful for users to perform healthcare themselves by providing information on serious diseases and lifestyle habits to be improved according to the clusters classified by PHR profile.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.83-91
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• 2011

Much research has been conducted on the e-learning systems for recommending a learning content to a student based on the difficulty of it. The difficulty is one of the most important factors for selecting a learning content. In the existing learning content recommendation systems, the difficulty of a learning content is determined by the creator. Therefore, it is not easy to apply a standard rule to the difficulty as it is determined by a subjective method. In this paper, we propose an ontology-based method for determining the difficulty of a learning content in order to provide an objective measurement. Previously, ontologies and knowledge maps have been used to recommend a learning content. However, their methods have the same problem because the difficulty is also determined by the creator. In this research, we use an ontology representing the IS-A relationships between words. The difficulty of a learning content is the sum of the weighted path lengths of the words in the learning content. By using this kind of difficulty, we can provide an objective measurement and recommend the proper learning content most suitable for the student's current level.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.525-531
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• 2009

Data mining is the process of extracting hidden patterns from collected data. At this time, for collected data which take important role as the basic information for prediction and recommendation, the process to discriminate incorrect data in order to enhance the performance of analysis result, is needed. The existing methods to discriminate unexpected data from collected data, mainly relies on methods which are based on statistics or simple distance between data. However, for these methods, the problematic point that even meaningful data could be excluded from analysis due that the environment and characteristic of the relevant data are not considered, exists. This study proposes a method to endow human heuristic knowledge with weight value through the comparison between collected data and human heuristic knowledge, and to use the value for creating a decision tree. The data discrimination by the method proposed is more credible as human knowledge is reflected in the created tree. The validity of the proposed method is verified through an experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.276-279
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• 2022

The 4th industrial revolution refers to the transition to a knowledge society in which the production speed of knowledge is fast and the proportion of the knowledge industry is greatly increased. The reorganization of the industrial structure and the change of occupations and jobs due to new technologies are bringing about changes in education, and the development of digital technology has made education that is borderless, individual and dynamic, becoming the new standard of education. With these changes, interest in nano-degrees on new technologies or micro-degrees focused on core courses rather than regular course degrees is increasing. As a representative example, Udacity in the United States has opened and operated online nanodegree courses related to jobs, and collaborates with major companies to develop and educate core training courses necessary for companies, thereby efficiently supporting companies in securing talent. With the revitalization of online vocational and job training, an environment has been prepared in which individuals can set goals for vocational competency development and continue their portfolio-based sustainable learning. However, for effective vocational education, automated and personalized educational content design should be preceded. To this end, in this paper, we propose a personalized career and course map recommendation system in the era of online learning

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Knowledge Management Research
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• v.16 no.3
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• pp.149-169
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• 2015

In this study, we try to understand the role of online social recommendation and the similarity of preferences between the recommender and the recommendee on consumer decisions in the framework of the two stage purchase decision-making process. Applying construal level theory to our context, we expect that the role of social recommendation and the similarity of preferences would vary over the stages in the two-stage decision making process. To test our hypotheses, we collected the data through an incentive compatible experiment, and analyzed the data with nested logit model. As a result, we found that the role of online social recommendation varies over the stages. Consumers take recommendation from similar others at the stage of consideration set formation, but no longer consider it at the stage of final choice. Consumers take recommendation from dissimilar others at the stage of consideration set formation. At the stage of final choice, however, consumers avoid choosing the option recommended by dissimilar others. The results of our study enrich the understanding about the role of social recommendation, and have implication to marketing practitioners who attempt to make online social recommendation system more efficient.