• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.115-127
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• 2021

It is not an easy task for a user to find the correct documents that a user really wanted at once from a vast amount of the search results. For this reason, various methods of recommending documents by taking the user's preferences into consideration based on the user's document browsing history have been proposed. However, the document recommendation methodology based on the document browsing history also has a limitation that only the information the user has viewed is utilized, but the intent of the user searching for the document is not fully utilized. Therefore, we propose a document recommendation method based on the user's search intent that utilizes information on "Why" the user reads the document, instead of the information on "Who" reads the document. In order to confirm the feasibility of the proposed methodology, an experiment was conducted by analyzing 239,438 actual user's search history of one of the most popular e-commerce platform companies in Korea. As a result, our methodology showed superior performance compared to the existing content-based or simple browsing history-based recommendation model.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.711-728
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• 2023

This study aims to validate the user experience associated with the personalized recommendation systems of fashion vertical platforms. The investigation focused on women aged 18 to 30 with prior experience using personalized fashion recommendation systems. The collected data were analyzed using SPSS 26.0 and AMOS 26.0, and the outcomes can be summarized as follows. Firstly, the diversity and usefulness of information quality exerted a positive effect on use satisfaction. Secondly, the affirmative impact of the reliability of system quality on user satisfaction was established, although stability was not confirmed. Thirdly, the study identified a favorable connection between ease-of-use of service quality and user satisfaction, while the influence of tangibles was unsubstantiated. Fourthly, the degree of self-reference was found to have a positive effect on user satisfaction. Fifthly, a constructive relationship emerged between user satisfaction and both continuous-use intention and recommendation intention. Lastly, there was a significant difference in the magnitude of the effect of ease-of-use on satisfaction according to self-regulatory focus. The findings of this study hold the potential to enhance the explanatory and predictive power of the field of consumer behavior within the novel shopping landscape of fashion vertical platforms.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Journal of Korea Multimedia Society
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• v.18 no.12
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• pp.1586-1593
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• 2015

Calligraphy contents(shortly called, CalliContents) depict the feature of communication media with artistic sentences or drawings before being processed into digital contents to become printed advertisement, visual design and entertainment products. As a fast growing business model, they can be applied to every single scope of all fields these days and each application case presented excellent effects to grab consumers' attention immediately. In this paper, we designed and produced an emotional bi-directional recommendation DIY calligraphy contents platform to consume created cultural contents and boost personalized contents industry that meets consumer's needs through both wired and wireless-based software with convergence of artistic and emotional calligraphy contents and ICT. For this works, we established for DIY calligraphy consumers a foundation of a virtuous circle of the CalliContents where various CalliContents are provided in on and offline environment and a third party target is opened at the CalliContents platform

• Journal of Smart Tourism
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• v.2 no.4
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• pp.35-43
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• 2022

Tourism businesses use chat services to provide immediate customer support and to help users navigate within a website, but there are more outcomes of this interaction that should be examined. The current study aimed to discover if the online travel chat service quality and information value of the online travel chat service lead to user satisfaction with the service and visit intention to a recommended destination by Korea Tourism Organization's 1330 Live Chat. The results indicate that information value (functional and innovation) and online travel chat service quality (reliability, assurance, and security) lead to satisfaction with the live chat service and visit intention to a recommended destination. The results can benefit practitioners who want to expand and improve their customer service interaction and recommendations, and to scholars who study the relationship between customer services in tourism recommendation and sales context.

• Informatization Policy
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• v.30 no.1
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• pp.3-22
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• 2023

This review article summarizes the issues around platform business with government regulation. We illustrated types and characteristics of platform business. We examined the current policy tools and government activities for regulating platform business in a digital economy. Policy tools of government regulation for platform business are not suitable or fit at times. We suggest an approach to look at the fit of platform business types and regulation policy tools. We also offer a policy recommendation to define situations whether to focus on minimizing conflicts among stakeholder or invite them to participate in anticipatory decision making process for new technology market.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.715-721
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• 2018

With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1009-1016
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• 2002

We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.4
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• pp.139-157
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• 2022

Finding and selecting proper reviewers is a burden on the publisher of the journal. In order to solve this problem, the online submission system started to recommend appropriate reviewers automatically. It includes a variety of new features, from recommending authors in the references of submitted papers as reviewers to finding similar papers by searching the citation index and suggesting reviewer candidates extensively. This study investigated how the online submission system provides functions such as recommendation of reviewers. As a result of examining major online submission systems, ScholarOne and Editorial Manager were recommending reviewer candidates by commercial citation index and review history platform. On the other hand, JAMS, a domestic online submission system, did not have any advanced functions such as recommendation of candidates for reviewers. Sooner or later, in Korea, it seems that more efforts should be made to improve the function of online submission system, such as recommending suitable reviewers for papers.