• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.258-267
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• 2021

This study attempted an empirical analysis study on the Meal Kit Product, whose interest and demand continued to increase according to the eating out trend in the Untact era. In addition, this study attempted to investigate the relationship between the factors of Home Meal Replacement Meal Kit Product Selection Attributes, Consumers Satisfaction, and Other Recommendation Intention. Convenience sampling was used for consumers with experience in using Meal Kit Products released by food service companies and start-up companies. The investigation period was conducted for about one month from July 01, 2020, and the final 285 copies were used for analysis. The SPSS 21.0 statistical package program was used to verify the hypothesis. As a result of the analysis, the price (β=.241), convenience (β=.317), and diversity (β=.191) of Hypothesis 1 had a significant effect on Consumers Satisfaction. Price (β=.482), convenience (β=.133), and diversity (β=.342) were found to have a significant effect on the intention to recommend others. It was analyzed that Hypothesis 3's Consumers Satisfaction (β=.443) had a significant effect on Other Recommendation Intention. Finally, through this study, we expect to be provided as basic research data related to Meal Kit Product. It is intended to be presented as a theoretical basis for the use of marketing and direction for the development of milk kit products for catering companies and restaurants.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.177-191
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is the most successful recommendation technology. Web usage mining and clustering analysis are widely used in the recommendation field. In this paper, we propose several hybrid collaborative filtering-based recommender procedures to address the effect of web usage mining and cluster analysis. Through the experiment with real e-commerce data, it is found that collaborative filtering using web log data can perform recommendation tasks effectively, but using cluster analysis can perform efficiently.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• The Korean Journal of Franchise Management
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• v.14 no.2
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• pp.1-17
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• 2023

Purpose: Due to the COVID-19 and inflation, participation sports companies, including fitness centers, are facing challenges. Since a fitness center must simultaneously manage facilities and operate services, both factors must be considered when developing a marketing strategy. Therefore, this study examines the effects of expanded marketing mix factors (price, physical evidence, place, people, product, and promotion) including facilities and services on the consumption behavior (satisfaction, recommendation intention, repurchase intention) of fitness center customers. Research design, data, and methodology: The data were collected from sample of 323 fitness club members in Seoul and analyzed with SPSS Win Ver.28.0 program. Result: The specific results of the study were as follows; First, extended marketing mix factors had significant positive (+) effect on satisfaction. Second, extended marketing mix factors had significant positive (+) effect on recommendation intention. Third, extended marketing mix factors had significant positive (+) effect on repurchase intention. Fourth, satisfaction had significant positive (+) effect on recommendation intention and repurchase intention. Conclusions: To encourage consumption behavior, it is necessary to convert existing customers into loyal ones by increasing satisfaction and establishing a virtuous cycle structure that recommends them to others while also improving repurchase intention.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.211-220
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• 2004

This paper suggests a collaborative business process between the companies that each has a restricted physical branch in its own area and wants to extend globally sales and delivery service. The companies integrate their business processes for sales and delivery using a shared product taxonomy table. We also suggest a similar product finding algorithm to make the product taxonomy table that defines product relationships to exchange them between the companies. The main idea of the proposed algorithm is using a multi-attribute decision making (MADM) to find the utility values of products in a same product class of the companies. Using the values we determine what products are similar. It helps the product manager to register the similar products into a same product sub-category. The companies then allow consumer to shop and purchase the products at their own residence site and deliver them or similar products to another sites.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.185-190
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• 2006

Most systems in ubiquitous computing analyze context information of users which have similar propensity with demographics methods and collaborative filtering to provide personalized recommendation services. The systems have mostly used static context information such as sex, age, job, and purchase history. However the systems have limitation to analyze users' propensity accurately and to provide personalized recommendation services in real-time, because they have difficulty in considering users situation as moving path. In this paper we use users' moving path of dynamic context to consider users situation. For the prediction accuracy we complete with a path completion algorithm to moving path which is inputted to RSOM. We train the moving path to be completed by RSOM, analyze users' moving pattern and predict a future moving path. Then we recommend the nearest product on the prediction path with users' high preference in real-time. As the experimental result, MAE is lower than 0.5 averagely and we confirmed our method can predict users moving path correctly.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.515-526
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• 2019

In the real world, the user's preference for a particular product is determined by many factors besides the quality of the product. The reflection of these external factors was very difficult because of various fundamental problems including lack of data. However, access to external factors has become easier as the infrastructure for public data is opened and the availability of evaluation platforms with diverse and vast amounts of data. In accordance with these changes, this paper proposes a recommendation system structure that can reflect the collectable factors that affect user's preference, and we try to observe the influence of actual influencing factors on preference by applying case. The structure of the proposed system can be divided into a process of selecting and extracting influencing factors, a process of supplementing insufficient data using sentence analysis, and finally a process of combining and merging user's evaluation data and influencing factors. We also propose a validation process that can determine the appropriateness of the setting of the structural variables such as the selection of the influence factors through comparison between the result group of the proposed system and the actual user preference group.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.15-20
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• 2023

This paper is a study on the development of an artificial intelligence (AI) system algorithm that recommends indirect advertising products suitable for character web dramas. The goal of this study is to increase viewers' content immersion and help them understand the story of the drama more deeply by recommending indirect advertising products that are suitable for writing lines for web dramas. In this study, we analyze dialogue and plot using the natural language processing model GPT, and develop two types of indirect advertising product recommendation systems, including prop type and background type, based on the analysis results. Through this, products that fit the story of the web drama are appropriately placed, allowing indirect advertisements to be exposed naturally, thereby increasing viewer immersion and enhancing the effectiveness of product promotion. There are limitations of artificial intelligence models, such as the difficulty in fully understanding hidden meanings or cultural nuances, and the difficulty in securing sufficient data for learning. However, this study will provide new insights into how AI can contribute to the production of creative works, and will be an important stepping stone to expand the possibilities of using natural language processing models in the creative industry.

• 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.

• The Research Journal of the Costume Culture
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• v.7 no.1
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• pp.141-153
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• 1999

To be successful, Korean exporters must understand how importers identify and select suppliers. This empirically based study investigate Korean exporter\`s perceptions of the supply selection criteria and information sources in US importers use. The specific purposes of this study were to identify the importance of the supply selection criteria and information sources and to examine the effects of the amount of export on the supply selection criteria and information sources in US importer use. For this study, data were obtained from Korean exporters by means of self-administered questionnaires. The questionnaires consisted of a series of statements covering a broad of specific selection criteria and information sources and exports\` characteristics including average annual amount of export. Using a base of 312 exporters, data were analysed by using mean, one-way ANOVA, and Ducan test. Major findings if this study summarized as follows; 1) Korean exporters perceived that US importers would place importances on product price, deliverly reliability, product wordsmanship-quality, and length of deliverly lead-time, in orders. Also, the more amount of export was, the higher product wordsmanship-quality, availability of piece goods and trims, and communication channel were importantly rate. 2) Korean exporters considered the third party sources, such as recommendation from trade association and buying office and import agency, as the most important information source in US importer use. Also, There was tendency that the more amount of export was, the more information sources on suppliers was importantly evaluated. From this study, several recommendation were suggested forward to encourage export in international apparel market.