• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.112-120
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• 2011

This research aims to show that the context adaptation system can be used to develop practical context-aware applications by developing a restaurant recommendation system based on location-awareness. In this research, we have designed and implemented a location-aware restaurant recommendation system which provides a customized restaurant recommendation service based on the user's current context. The context-adaptation engine adapts the application program according to the policy file as contexts are changed, and the application provides restaurant recommendation service based on the changed context like location.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.396-404
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• 2016

In this paper, the customized sport convergence contents curation system is proposed for activation of sport for all. The proposed system collects and analyzes profile of social sports group(club, society, etc.) for recommend optimized sport convergence contents to user. And the survey is progressed for research problem of existed service and usefulness of proposed service. The proposed system and service are expected to design innovative new service model for activate of sport for all.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.61-68
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• 2021

The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.460-461
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• 2018

The traditional recommendation system is developed on the assumption that users behave independently, and have problem of readability and efficiency are inferior due to simply sort products or lack of function for associate product attributes with user's taste. To solve this problem in this study we propose a system that provides user customized information that the analysis of the unstructured review data with the purchase histories of users processed with meaningful information after crawling product review data using text mining with R. This allows to help user make decisions can be provided only necessary information without analyze massive amounts of products review data.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.289-296
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• 2015

The explosive growth of information has been difficult for users to get an appropriate information in time. The various ways of new services to solve problems has been provided. As customized service is being magnified, the personalized recommendation system has been important issue. Collaborative filtering system in the recommendation system is widely used, and it is the most successful process in the recommendation system. As the recommendation is based on customers' profile, there can be sparsity and cold-start problems. In this paper, we propose personalized movie recommendation system using collaborative filtering techniques and context-based techniques. The context-based technique is the recommendation method that considers user's environment in term of time, emotion and location, and it can reflect user's preferences depending on the various environments. In order to utilize the context-based technique, this paper uses the human emotion, and uses movie reviews which are effective way to identify subjective individual information. In this paper, this proposed method shows outperforming existing collaborative filtering methods.

• Journal of Information Technology and Architecture
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• v.9 no.1
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• pp.57-66
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• 2012

Recently, with a rapidly growing of the mobile content market, a variety of mobile-based applications are being launched. But mobile devices, compared to the average computer, take a lot of effort and time to get the final contents you want to use due to the restrictions such as screen size and input methods. To solve this inconvenience, a recommender system is required, which provides customized information that users prefer by filtering and forecasting the information.In this study, an tailored multi-information recommendation system utilizing a Personalized information recommendation system on smartphone is proposed. Filtering of information is to predict and recommend the information the individual would prefer to by using the user-based collaborative filtering. At this time, the degree of similarity used for the user-based collaborative filtering process is Euclidean distance method using the Pearson's correlation coefficient as weight value.As a real applying case to evaluate the performance of the recommender system, the scenarios showing the usefulness of recommendation service for the actual restaurant is shown. Through the comparison experiment the augmented reality based multi-recommendation services to the existing single recommendation service, the usefulness of the recommendation services in this study is verified.

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.2
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• pp.59-72
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• 2022

The online fashion market in the 21st century has shown rapid growth. Against this backdrop, using consumer activity data to provide customized customer services has emerged as a viable business model that draws attention. Algorithm-based personalized recommendation services are a good example. But their application in fashion products has clear limitations. It is not easy to identify consumers' perceptions of the attributes of fashion, which are various, hard to define, and very sensitive to trends. So there is a need to compile data on consumers' underlying awareness and to carry out defined research to increase the utilization of such services in the fashion industry and further engage consumers. This research aims to classify the attributes and types of fashion products and to identify consumers' perceptions of a given situation where a recommendation service is offered. To find out consumers' perceptions of and satisfaction with recommendation services, an online and mobile survey was conducted on women in their 20s and 30s, a group that uses recommendation services frequently. A total of 455 responses were used for analysis. SPSS 28.0 was used, combined with Conjoint Analysis and multiple regression, to analyze data. The study results could provide insights into a better understanding of recommendation services and be used as basic data for companies to identify consumers' preferences and draw up a detailed strategy for market segmentation.

• The Mathematical Education
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• v.59 no.4
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• pp.373-387
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• 2020

The purpose of this study is to verify the effects of personalized learning platforms applied with machine learning recommendation modules that upgrade recommended algorithms by themselves through learning big data analysis on students' learning time, self-directed learning ability, mathematics achievement, and attitudes toward mathematics, and the correlation between them. According to the study, customized learning affected learning time, self-directed learning ability and mathematics attitude, while learning time affected self-directed learning ability. Self-directed learning ability has had a significant impact on the attitude of mathematics and mathematical achievements. As a result of the mediated effectiveness test, the indirect impact of customized learning on mathematics attitude and mathematics performance was significant through the medium of learning time and self-directed learning ability.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.3
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• pp.343-372
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• 2021

The purpose of this study is to select keywords that can recommend books by personality type, and to test whether the chosen keywords can be actually used in the categorization and customized recommendation of books for each personality type. To achieve the research goal, this study chose books that match the level of fifth and sixth grade elementary school students and first grade middle school students and commissioned an expert group to categorize the books into groups that are preferred by each personality type. As a result of the classification, half of the books in which more than five experts agreed showed high consensus. In addition, the results of classifying books by personality type with keywords extracted by the automatic word extraction system by collecting the book review data of the selected books were similar to the results of the final judgement by the expert group, except for a few books. In conclusion, this study proved that it is possible to classify and recommend books that are likely to be preferred by different personality types using review keywords.