• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.9_10
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• pp.1352-1363
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• 2007

The purpose of this study is to understand consumer needs through fashion sensibilities on Character T-shirts. This study suggests the basis of planning effective design of Character T-Shirts by categorizing. The results were summarized as follows: 1. Fashion sensibility factors such as aestheticism, visibility, cutesiness, flexibility occupied 57.2% of the total. 2. The types of the Character T-Shirts were classified into four groups. The four types showed significant differences in all fashion sensibility. Aestheticism had its highest and lowest values in types 3 and 4, respectively; visibility in types 4 and 1, respectively; cutesiness in types 2 and 4, respectively; and flexibility in types 2 and 1. respectively. 3. As for the relation of consumer sensibility to fashion sensibilities, impulse related to eight adjectives; buying to nine adjectives; and recommendation to twelve adjectives. Impulse, buying and recommendation related to aestheticism and visibility.4. In the demographical aspect of fashion sensibilities and consumer sensibilities, significant differences found in age, gender, job and academic level. Therefore, the results of this study can be used as criteria of improving fashion sensibility consumer sensibility of Character T-Shirts. Especially, enhanced comsumer sensibility is expected by the elimination of texts and the choice of preferred character actions and vivid warm colors.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.437-442
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• 2021

In this study, a recommendation algorithm was implemented by learning a deep learning-based classification model for consumer data. For this purpose, a meaningful result is presented as a result of learning using ResNet50, which is commonly used in classification tasks by converting user data into images.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2004.11a
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• pp.42-42
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• 2004

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.31-49
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• 2023

With the growth of the food-catering industry, consumer preferences and the number of dine-in restaurants are gradually increasing. Thus, personalized recommendation services are required to select a restaurant suitable for consumer preferences. Previous studies have used questionnaires and star-rating approaches, which do not effectively depict consumer preferences. Online reviews are the most essential sources of information in this regard. However, previous studies have aggregated online reviews into long documents, and traditional machine-learning methods have been applied to these to extract semantic representations; however, such approaches fail to consider the surrounding word or context. Therefore, this study proposes a novel review textual-based restaurant recommendation model (RT-RRM) that uses deep learning to effectively extract consumer preferences from online reviews. The proposed model concatenates consumer-restaurant interactions with the extracted high-level semantic representations and predicts consumer preferences accurately and effectively. Experiments on real-world datasets show that the proposed model exhibits excellent recommendation performance compared with several baseline models.

• Journal of Distribution Science
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• v.22 no.9
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• pp.39-48
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• 2024

Purpose: The purpose of this study is to collect and analyze a large amount of data from online ticket distribution platforms that offer multiple airlines and different routes so that they can improve their ticket distribution marketing strategies and provide services that are more suitable for consumer's needs. The results of this study will help airlines improve the quality of their online platform services to provide more benefits and convenience by providing access to multiple airlines and routes around the world on one platform. Research design, data and methodology: For the study, 200 people completed the survey between May 1 and June 15, 2024, of which 191 copies were used in the study. Results: The hypothesis testing results of this study showed that among the components of the recommendation algorithm, decision comport, novelty, and evoked interest recurrence had a positive effect on perceived recommendation quality, but curiosity did not have a positive effect on recommendation quality. The perceived recommendation quality of the online platform positively influenced recommendation satisfaction, and the higher the perceived recommendation quality, the higher the intention to continue the relationship. Finally, higher recommendation satisfaction was associated with higher relationship continuation intention. Conclusion: it's important to continue researching online ticketing platforms. Online platforms will also need to be systems that use technology and data analytics to provide a better user experience and more benefits.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.95-117
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• 2022

With the emergence of various media types due to the development of Internet technology, advertisers have difficulty choosing media suitable for corporate advertising strategies. There are challenging to effectively reflect consumer contextual information when advertising media is selected based on traditional marketing strategies. Thus, a recommender system is needed to analyze consumers' past data and provide advertisers with personalized media based on the information consumers needs. Since the traditional recommender system provides recommendation services based on quantitative preference information, there is difficult to reflect various contextual information. This study proposes a methodology that uses deep learning to recommend personalized media to advertisers using consumer contextual information such as consumers' media viewing time, residence area, age, and gender. This study builds a recommender system using media & consumer research data provided by the Korea Broadcasting Advertising Promotion Corporation. Additionally, we evaluate the recommendation performance compared with several benchmark models. As a result of the experiment, we confirmed that the recommendation model reflecting the consumer's contextual information showed higher accuracy than the benchmark model. We expect to contribute to helping advertisers make effective decisions when selecting customized media based on various contextual information of consumers.

• 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.

• Information Systems Review
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• v.25 no.1
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• pp.27-46
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• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• Journal of the Ergonomics Society of Korea
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• v.27 no.4
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• pp.85-94
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• 2008

Internet shopping has been getting more rousing due to extension of supply with PC(personal computer) and a rapid rise of use of internet. Some companies have been continually researching in how to serve individuals with each ordered information, which aimed at getting ordinary customers to induce to be loyal customers. For that, there is progress of a service of a web-recommendation which considers individual attribution. This study is suggested a method which is a service of the web-recommendation by access to sensibility ergonomics approach. Previous studies established that service had a weak point. It did not manage to realize new needs of customers. Proposed service of the web-recommendation has been designed, which preferentially propose goods included customer's sensibility to the customer who wants it. This study is expected that it will encourage a rise of products' purchasing power of customers, make an increase in a profit of both sellers and people who operate electric commercial and satisfaction of customers will go up in the same. Also, products accord with sensibility of customers will be recommended customers by the suggested service of the web-recommendation. In addition, there will be a decline of time-consuming about making a choice among some products.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.36-44
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• 2005

Recommender services are being used by an ever-increasing number of mobile commerce applications to help consumers find items to purchase with the use of the situated contexts. In this paper, we propose a new leveled recommendation for context-aware mobile commerce. This enables a consumer to obtain relevant information efficiently by using leveled recommendation, patterns and prefetching. This paper describes the method and application scenarios. Several experiments are performed and the results verify that the proposed method's recommendation performance is better than other existing methods.