• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.999-1006
• /
• 2022

The use of appropriate fonts is not only an aesthetic point of view, but also a factor influencing the reinforcement of meaning. However, it is a difficult process and wastes a lot of time for general users to choose a font that suits their needs and emotions. Therefore, in this study, keywords and fonts to be used in the experiment were selected for emotion-based font recommendation, and keyword values for each font were calculated through an experiment to check the correlation between keywords and fonts. Using the experimental results, a prototype of a keyword-based font recommendation system was designed and the possibility of the system was tested. As a result of the usability evaluation of the font recommendation system prototype, it received a positive evaluation compared to the existing font search system, but the number of fonts was limited and users had difficulties in the process of associating keywords suitable for their desired situation. Therefore, we plan to expand the number of fonts and conduct follow-up research to automatically recommend fonts suitable for the user's situation without selecting keywords.

• Journal of Convergence for Information Technology
• /
• v.9 no.5
• /
• pp.1-6
• /
• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• Journal of Intelligence and Information Systems
• /
• v.10 no.3
• /
• pp.133-146
• /
• 2004

By the development of the network technology, the types and amount of information that users keep in contact with have been dramatically increased. As a result, users are consuming a lot of time and energy to find needed information. On this, this article presents a new methodology that can efficiently manage their information within small cost by using content-based recommendation method and keyword affinity method. By using keyword affinity method, this methodology solves the content-based recommendation method's weak point that the performance is not good within the environment that the preferences of users are rapidly changing and new contents are created continuously and the accuracy level is low until the information of preferences are sufficiently gathered. This article carried out research on the personal e-mail environment where new information is frequently created and disappeared. Also this article assists folder recommendation for the efficient management of e-mail and verified the methodology mentioned above by an experiment to compare the performance of existing folder recommendation methods with the performance of this new method.

• The Journal of Information Systems
• /
• v.27 no.4
• /
• pp.71-96
• /
• 2018

Purpose Recently, social interest in the convergence research is at its peak. However, contrary to the keen interest in convergence research, an infrastructure that makes it easier to recruit researchers from other fields is not yet well established, which is why researchers are having considerable difficulty in carrying out real convergence research. In this study, we implemented a researcher recommendation system that helps researchers who want to collaborate easily recruit researchers from other fields, and we expect it to serve as a springboard for growth in the convergence research field. Design/methodology/approach In this study, we implemented a system that recommends proper researchers when users enter keyword in the field of research that they want to collaborate using word embedding techniques, word2vec. In addition, we also implemented function of keyword suggestions by using keywords drawn from LDA Topicmodeling Algorithm. Finally, the UI of the researcher recommendation system was completed by utilizing the collaborative messenger Slack to facilitate immediate exchange of information with the recommended researchers and to accommodate various applications for collaboration. Findings In this study, we validated the completed researcher recommendation system by ensuring that the list of researchers recommended by entering a specific keyword is accurate and that words learned as a similar word with a particular researcher match the researcher's field of research. The results showed 85.89% accuracy in the former, and in the latter case, mostly, the words drawn as similar words were found to match the researcher's field of research, leading to excellent performance of the researcher recommendation system.

• Journal of Korea Society of Industrial Information Systems
• /
• v.24 no.6
• /
• pp.11-24
• /
• 2019

The tendency of buyers of web information is changing from the cost-effectiveness to the cost-satisfaction. There is such tendency in the recommendation of multimedia contents, some of which are folksonomy-based recommendation services using mood. However, there is a problem that they does not consider synonyms. In order to solve this problem, some studies have solved the problem by defining 12 moods of Thayer model as AV values (Arousal and Valence), but the recommendation performance is lower than that of a keyword-based method at the recall level 0.1. In this paper, we propose a method based on using mood vector of multimedia contents. The method can solve the synonym problem while maintaining the same performance as the keyword-based method even at the recall level 0.1. Also, for performance analysis, we compare the proposed method with an existing method based on AV value and a keyword-based method. The result shows that the proposed method outperform the existing methods.

• Journal of Information Technology Applications and Management
• /
• v.12 no.4
• /
• pp.45-57
• /
• 2005

In order to provide personalized knowledge recommendation services, most web portals for organizational knowledge management use category or keyword information that portal users explicitly express interests in. However, it is usually difficult to collect correct preference data for all users with this approach, and, moreover, users' preferences may easily change over time, which results In outdated user profiles and impaired recommendation qualify. In order to address this problem, this paper suggests knowledge recommendation methods for portals using user profiles that are automatically constructed from users' activities such as posting or uploading of articles and documents. The result of our experiment shows that the Proposed method can provide equivalent performance with the manual category or keyword selection method.

• Journal of Korea Multimedia Society
• /
• v.22 no.11
• /
• pp.1313-1323
• /
• 2019

With the development of the Internet and the increase of smart phones, various services considering user convenience are increasing, so that users can check news in real time anytime and anywhere. However, online news is categorized by media and category, and it provides only a few related search terms, making it difficult to find related news related to keywords. In order to solve this problem, we propose a method to recommend related documents more accurately by applying Doc2Vec similarity to the specific keywords of news articles and weighting the title and contents of news articles. We collect news articles from Naver politics category by web crawling in Java environment, preprocess them, extract topics using LDA modeling, and find similarities using Doc2Vec. To supplement Doc2Vec, we apply TF-IDF to obtain TC(Title Contents) weights for the title and contents of news articles. Then we combine Doc2Vec similarity and TC weight to generate TC weight-similarity and evaluate the similarity between words using PMI technique to confirm the keyword association.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.05a
• /
• pp.346-348
• /
• 2022

Media Contents Recommendation Web Service (service name 'mobodra') is a web service that analyzes media types and genre tastes for each user and recommends content accordingly. Users select some of the works randomly provided on the web when signing up for membership and analyze their tastes based on this. Based on this analysis, preferred content for each user is recommended. In this paper, we implement a content recommendation algorithm through item-based collaborative filtering. When the user's activity data or preference is re-examined, the above process is executed again to update the user's taste.

• Journal of the Korea Convergence Society
• /
• v.13 no.3
• /
• pp.133-146
• /
• 2022

Despite the fact that Internet users around the world watch YouTube every day, very few users accurately recognize the recommendation algorithm for search results, and Google and YouTube are not disclosing it. Researchers tried to explore the undisclosed algorithm of YouTube in a reverse engineering design method, find key factors, and check the logical structure in which media platform operators recommend keyword search results and arrange them on the screen. Therefore, researchers studied the basic content priority factors through several months of discussion and data collection, and tried to reverse engineer the influencing factors based on the recommendation results according to male and female gender among the collected keyword search results. Although researchers' design only analyzed some of the almost infinite level of data uploaded and viewed for more than hundreds of hours every hour, these exploratory attempts will study media platform algorithms in the future, understand the intentions of operators, and protect users. thought it could be done.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.127-138
• /
• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.