• Journal of Service Research and Studies
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• v.13 no.3
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• pp.82-104
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• 2023

This study analyzes the effect of users' experiences using AI chatbot services on relationship quality and behavioral intention. For the study, a survey was conducted on users who experienced AI chatbot services, and the research hypothesis was verified by analyzing the final 299 copies of valid data. As a result of the analysis, it was confirmed that satisfaction and trust, which are the relationship quality dimensions of AI chatbot service, were formed in users through the cognitive experience, emotional experience, and relational experience. In addition, it was confirmed that satisfaction and trust have a positive effect on the intention to continue using and recommending AI chatbot services, which correspond to the level of consumers' behavioral intentions, respectively. In addition, in terms of relationship quality, it was significant in all paths of the road of behavior, but in satisfaction, the path coefficient of the road of continuous use of AI chatbot and recommended road was significantly higher than the path coefficient in trust. This study provided a theoretical foundation that the relationship with relationship quality that affects behavioral intention also affects AI chatbot services in the online environment, and it is significant in that it suggests that relationship quality is an important mediating factor in establishing long-term relationships with consumers.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.127-138
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• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.113-122
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• 2011

Social Network Service(SNS) has become a hot trend as a web service which helps users construct social relationships in the web and enables online communication. The information about user activities and behaviors obtained from the SNSs is expected to be an useful knowledge source for other services such as recommendation services. Most of previous researches on SNS rely on analyzing overall network topology and surveying the activities in a one-dimensional aspect. This paper propose a system for measuring multi-dimensional interaction through the activities in a SNS. The proposed system delivers an unified profile (consisting of profile and multi-dimension interaction) model from user-activities in Twitter.com. At the experimental section, some meaningful perspectives on a set of the unified profiles are described.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Korean Library and Information Science Society
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• v.53 no.1
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• pp.231-263
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• 2022

The understanding and capability to utilize artificial intelligence (AI) incorporated technology has become a required basic skillset for the people living in today's information age, and various members of the university have also increasingly become aware of the need for AI education. Amidst such shifting societal demands, both domestic and international university libraries have recognized the users' need for educational content centered on AI, but a user-centered service that aims to provide personalized recommendations of digital AI educational content is yet to become available. It is critical while the demand for AI education amongst university students is progressively growing that university libraries acquire a clear understanding of user intention towards an AI educational content recommender system and the potential factors contributing to its success. This study intended to ascertain the factors affecting acceptance of such system, using the Extended Technology Acceptance Model with added variables - innovativeness, self-efficacy, social influence, system quality and task-technology fit - in addition to perceived usefulness, perceived ease of use, and intention to use. Quantitative research was conducted via online research surveys for university students, and quantitative research was conducted through written interviews of university librarians. Results show that all groups, regardless of gender, year, or major, have the intention to use the AI-related Educational Content Recommendation System, with the task suitability factor being the most dominant variant to affect use intention. University librarians have also expressed agreement about the necessity of the recommendation system, and presented budget and content quality issues as realistic restrictions of the aforementioned system.

• Information Systems Review
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• v.4 no.2
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• pp.155-168
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• 2002

This paper examines whether the functions of eCRM solutions affect the site visit and purchase by customers. The functions of eCRM solutions are extracted and classified into three categories of c-marketing, e-sales, and e-service. E-marketing includes campaign/event marketing, e-mail marketing, and questionnaire marketing; e-sales consists of recommendation system and incentive/discount promotion.; e-service is composed of e-mail call center and web call center. From the online survey, 146 responses are collected and analyzed. The analysis shows that the level of experience in campaign/event marketing, e-mail marketing, e-mail call center, and web call center significantly affect the website visit by customers and that the level of experience in all eCRM functions except e-mail marketing significantly affect the purchase by customers. The effects of those functions in eCRM on the website visit are moderate, while the effects of the functions on the purchase are low. The results from this study imply that eCRM needs to strengthen the effect on the purchase with more thorough analysis of the customer profile.

• The Journal of Korean Association of Computer Education
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• v.21 no.5
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• pp.61-68
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• 2018

Collaborative filtering is a fundamental technique implemented in many commercial recommender systems and provides a successful service to online users. This technique recommends items by referring to other users who have similar rating records to the current user. Hence, similarity measures critically affect the system performance. This study addresses problems of previous similarity measures and suggests a new similarity measure. The proposed measure reflects the subjectivity or vagueness of user ratings and the users' rating behavior by using fuzzy logic. We conduct experimental studies for performance evaluation, whose results show that the proposed measure demonstrates outstanding performance improvements in terms of prediction accuracy and recommendation accuracy.

• Information Systems Review
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• v.20 no.1
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• pp.1-16
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• 2018

The development and generalization of the Internet increased the popularity of food delivery service applications in Korea. The food delivery market based on online-to-offline service is growing rapidly. This study compares the usability of Korean food delivery service application between that of Chinese food delivery service application. This study suggests improvement points for Korean food delivery service applications. To conduct this study, we explore the status of various food delivery service applications and conduct interviews and surveys based on the honeycomb model developed by Peter Morville. This study obtained the following results. First, all restaurants participating in the Korean food delivery service must be able to accept order through the application. Second, the shopping cart function must be able to accept order of all restaurants simultaneously. Third, when users look for menu recommendation, their purchase history and shopping cart functions should appear at the first page of the website. Users should be able to perceive the improved usability of the website using those functions. Fourth, when the search window is fixed on the top of each page, users should be able to find the information they need. Fifth, the application must allow users to find the exact location of the delivery person and the estimated delivery time. Finally, the restaurants'address should be disclosed and fast delivery time should be confirmed to enhance users'trust on the application. This study contributes to academia and industry by suggesting useful insight into food delivery service applications and improving the point of food delivery service application in Korea.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.1-10
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• 2020

The performance of the personalized product recommendation system for offline shopping malls is poor compared with the one using online environment information since it is difficult to obtain visitors' characteristic information. In this paper, a mobile robot platform is suggested capable of recommending personalized advertisement using customers' sex and age information provided by Face API of MS Azure Cloud service. The performance of the developed robot is verified through locomotion experiments, and the performance of API used for our robot is tested using sampled images from open Asian FAce Dataset (AFAD). The developed robot could be effective in marketing by providing personalized advertisements at offline shopping malls.