• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.273-285
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• 2024

With the active utilization of Online Judge (OJ) systems in the field of education, various studies utilizing learner data have emerged. This research proposes a problem recommendation based on a user-based collaborative filtering approach with learner data to support learners in their problem selection. Assistance in learners' problem selection within the OJ system is crucial for enhancing the effectiveness of education as it impacts the learning path. To achieve this, this system identifies learners with similar problem-solving tendencies and utilizes their problem-solving history. The proposed technique has been implemented on an OJ site in the fields of algorithms and programming, operated by the Chungbuk Education Research and Information Institute. The technique's service utility and usability were assessed through expert reviews using the Delphi technique. Additionally, it was piloted with site users, and an analysis of the ratio of correctness revealed approximately a 16% higher submission rate for recommended problems compared to the overall submissions. A survey targeting users who used the recommended problems yielded a 78% response rate, with the majority indicating that the feature was helpful. However, low selection rates of recommended problems and low response rates within the subset of users who used recommended problems highlight the need for future research focusing on improving accessibility, enhancing user feedback collection, and diversifying learner data analysis.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.890-915
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• 2022

Although existing studies on e-health have usually focused on e-health services adoption intention, there is a dearth of studies on the barriers that affect e-health services retention intention especially in India. Additionally, although studies have mostly focused on utilizing expectation-confirmation model to understand innovation related barriers, innovation resistance theory (IRT) has been overlooked. As Indian e-health service providers face stiff challenges due to customer's unwillingness to continue using the service, there is a need to bridge the research gap that exists in this context. This mixed-method study, based on responses received from 289 participants and 1154 online negative reviews from e-Health providers in India, examines the barriers from the IRT stance. Results of this study reveal a notable negative association between tradition, value and financial barrier and intention to continue using e-health services. Additionally, continuance intention affects recommendation. The study concludes with various implications and scope for future research.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.19-34
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• 2022

Purpose - The purpose of this study is to examine whether a psychological concept enhances healthcare users' service experience. Specifically, the study proposes and empirically examines a model of perceived control in which the user's sense of control is postulated as exerting positive influences upon his/her motivation, self-efficacy associated with his/her role as a patient, and satisfaction with his/her medical service experience. Methodology - Data were collected by a professional research company, using an online survey method. Participants of the study included adults nineteen years or older who had visited a medical service institute at least once during the previous one-year period. For the test of the research hypotheses, structural equation modeling using AMOS was used. Findings - Findings of this study denote a unique insight into the users' comprehension of medical service experiences and their behaviors. First, the concept of perceived control is identified as a factor that enhances the quality of individuals' medical service experiences. A sense of control directly influences medical users' self-efficacy to comply with doctor's recommendations, their motivation to comply with doctor's recommendations, and their satisfaction with the medical service experience. Second, one's perceived self-efficacy is found to exert positive influences upon both motivation and satisfaction. Third, one's motivation to comply with the doctor's recommendation is found to exert a positive influence upon one's satisfaction. Additionally, perceived control is found to exert an indirect influence upon medical service users' satisfaction through the mediation of both self-efficacy and motivation. Research Implications - The findings of the study support the notion that perception of control among medial service users enhances their service experience as patients. The main thrust of this study suggests that it is necessary for healthcare practitioners to consider implementing service encounter strategies that purposefully enhance the sense of control among their patients. The identification of significant inter-relationships among perceived control, motivation, self-efficacy, and satisfaction among medical service customers should also serve as a meaningful seed for further research pursuits.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1567-1573
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• 2020

The development of Internet technology has brought the era of one-man media. An individual produces content on user own and uploads it to related online services, and many users watch the content of online services using devices that allow them to use the Internet. Currently, most users find and watch content they want through search functions provided by existing online services. These features are provided based on information entered by the user who uploaded the content. In an environment where content needs to be retrieved based on these limited word data, user unwanted information is presented to users in the search results. To solve this problem, in this paper, the system actively analyzes the video in the online service, and presents a way to extract and reflect the characteristics held by the video. The research was conducted to extract morphemes based on the story content based on the voice data of a video and analyze them with big data technology.

• Journal of Distribution Science
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• v.18 no.5
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• pp.25-33
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• 2020

Purpose: The use of e-Samsat services in East Java has not been significant in the amount of use of its services for tax payments as a whole. The purpose of this study is to analyze what factors East Java e-Samsat services practice and the existence of recommendations as a basis for government decisions to improve the quality of East Java e-Samsat services. Research design, data and methodology: Our model hypothesizes that three key factors determine the intention to use e-samsat platform such as: trust, awareness, ease to use. Data collection methods by distributing questionnaires and interviews. Results: The results of the study provide two findings, firstly, Trust, Ease of Use, Awareness directly or indirectly affects the Intention to Use the East Java e-Samsat service for motor vehicle taxpayers. Thus it is essential to pay attention to these three variables in terms of clarity, reliability, and timeliness as a recommendation to improve the quality of East Java e-Samsat services.. Conclusions: The results of this study can be applied and developed in other countries besides Indonesia with the same cultural patterns. Several variables have been measured in previous studies in several Asian continent countries.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.1
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• pp.80-98
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• 2022

Artificial intelligent chatbot services have recently become common in fashion e-retailing and are expected to improve online shopping by making it easy to recommend products. This study examines whether the perceived quality of a fashion chatbot affects consumers' trust and perception of usefulness, which in turn influences satisfaction and intention to use, in accordance with the information system success model. The study also investigates differences in perceived quality and consumer response variables between high and low groups of self-efficacy. A total of 341 consumers participated in an online survey. The results revealed that information quality and system quality had a significant impact on perceived usefulness and trust, and that service quality significantly impacted trust. Perceived usefulness and trust had a positive effect on consumer satisfaction, which in turn had a positive effect on intention to use. In addition, the findings revealed that people who had higher self-efficacy showed higher scores on perceived usefulness, trust, satisfaction, and intention to use chatbots as compared to people who had lower self-efficacy. This study suggested theoretical implications by applying the information system success model theory to fashion chatbot studies. It also suggested practical implications for e-commerce marketers developing retail strategies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.53-62
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• 2020

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.217-232
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• 2020

The purpose of this study is to organize prior research related to e-commerce activation factors available to stakeholders in the online distribution industry, and to conduct FGI with e-commerce experts to calculate the importance of each factor in order based on the 3 Level Service Model of Kwon Hyeog-in (2010), the key factors derived through the preceding study and the FGI were structured and the weighting of each factor was derived using AHP methodology. In the higher factors, the private sector (0.542) > communes (0.237) > public (0.2222) appeared to be important. Sub-factories included 'search service development' (0.0970)', 'recommendation service development (0.0805)', 'consumer needs analysis (0.0534)', 'consumer consumption pattern analysis (0.0505)' and 'other platform-linked service development (0.0450)', in the order of weighting down, indicating each of the factors within the top 15 priority. The results of this study will be utilized throughout the e-commerce industry as well as e-commerce enterprises, providing an academic foundation for the rapidly growing e-commerce ecosystem.