• Asia pacific journal of information systems
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• v.29 no.3
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• pp.468-488
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• 2019

As the growth of online travel agencies (hereafter OTAs) accelerates, competition among hotels to gain exposure on the first page of OTA websites, and the financial burden, such as commissions hotels have to pay in return, are increasing. Therefore, to facilitate successful management in the tourism industry, it is important to establish what makes people continue the practice of using OTAs to book rooms in hotels and other accommodation outlets. By adopting the heuristic-systematic model (HSM), this study explores the factors that drive consumers' continued use of OTA and classifies them into heuristic cues (brand awareness, cost saving, and scarcity message) and systematic cues (recommendation quality and the ability to provide reputation). Furthermore, we divided the sample based on the location of hotels within and outside Korea, and investigated the different roles of the cues between two models. The results are expected to provide theoretical and practical implications for both OTAs and hotels.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.63-70
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• 2022

Fashion is a field in which personal taste acts as the first criterion for purchase, and it is being refined as an important strategy to increase purchase conversion on mobile. Although related studies have been conducted, there are insufficient studies to confirm this according to the detailed purchasing journey of consumers. The purpose of this study is to examine whether the evaluation of user experience factors of personalized recommendation service differs by purchase journey, and to reveal whether it affects purchase intention and satisfaction. Variety, reliability, and convenience showed a significant difference at the level of 0.001% and usefulness at the level of 0.05%. Satisfaction levels were different for each stage, such as novelty and usefulness in the cognitive and interest stage, and high reliability and diversity in the search stage. It has theoretical significance in that it enhances the understanding of the purchase journey by revealing that there is a difference in user evaluation of the personalized recommendation service, and it has practical significance in that it suggests the direction of improvement of the personalized recommendation service strategy. If research on effectiveness is conducted in the future, it will be able to contribute to an advanced strategy.

• Culinary science and hospitality research
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• v.23 no.6
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• pp.27-35
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• 2017

The purpose of this study was to examine the causal relationship among restaurant selection attributes and customer satisfaction and recommendation tastes for young people in their twenties who use tickets for Rail-ro. Data collection was conducted to utilize questionnaire survey with online and offline distribution. The collected data were analyzed using a statistical program SPSS 21.0 with frequency analysis, reliability analysis, factor analysis, and regression analysis. The results of the study showed that Internet search is the most common source of information about restaurants during the trip, and restaurant choice attributes have an important impact on customer satisfaction, food quality, employee service and reputation, but hygiene did not have a big effect on customer satisfaction. In addition, customer satisfaction has a significant effect on recommendation intention. Concluding the results from this study, it investigated the significant attributes for customers selection of restaurants and provide meaningful advice for market managers to make useful marketing strategies to attract more clients and augment economic benefits.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.501-506
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• 2022

The prolonged of the Post Corona, many Cosmetic company launched various online services. In this paper, consider about the quality of product recommendation using personal color detecting technology. Using the detecting tool which is most widely used by cosmetic company. we will do a lot of testing with this tool and also testing with color detecting equipment. For precise experimental results, it was conducted in a consistent experimental environment. This experiment can be a foundation that can be well used for the expansion of personalized product recommendation services according to the current image-based skin color measurement.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.1-8
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• 2023

Cross-domain recommendation is a method that shares related user information data and item data in different domains. It is mainly used in online shopping malls with many users or multimedia service contents, such as YouTube or Netflix. Through K-means clustering, embeddings are created by performing clustering based on user data and ratings. After learning the result through a transformer network, user satisfaction is predicted. Then, items suitable for the user are recommended using a transformer-based recommendation model. Through this study, it was shown through experiments that recommendations can predict cold-start problems at a lesser time cost and increase user satisfaction.

• Information Systems Review
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• v.23 no.3
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• pp.51-75
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• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• Journal of Korean Library and Information Science Society
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• v.53 no.4
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• pp.189-209
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• 2022

The purpose of this study is to analyze the content of the online book curation services and recommended books list by public libraries in Korea and to identify their properties. The case for analysis is a list of 11,447 recommended books provided by 35 online book curation services collected from 23 public libraries and the main results of the study are as follows. Only few case libraries were presenting recommendation themes, and recommendation targets were most often not specific, and the recommendation cycle of books was the most monthly. In general, books recommended for book curation do not overlap with each other, but there was overlap in the field of literature (novels) published in 2019~2021. For recommended books, the proportion of books published by some publishers was high, and books published in 2019~2021 were the most common. The subject areas analyzed based on the KDC 6th ed were literature the most. Readers analyzed by ISBN were of in the order of cultural books and children's books, and the type of publication was in the order of books, pucture books, and comics. Based on these research results, it was required to develop guidelines for online book curation service for public libraries and build a platform to share with libraries.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.191-205
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• 2005

This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.

• Smart Media Journal
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• v.10 no.1
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• pp.116-123
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• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.