• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.199-223
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• 2024

This study presents book curation services as one of the ways to establish the social role and status of public libraries in a changing social environment. It aims to identify the quality factors that influence user satisfaction and the intention to continue using public library book curation services. For this purpose, the quality factors were limited to information quality through a literature review on curation services in libraries and various fields, from which specific factors were extracted. A questionnaire survey and statistical analysis were then conducted among public library users. The results showed that among the information quality factors, variety and interestingness affect user satisfaction, while discoverability and usefulness impact both user satisfaction and the intention to continue using the services. Based on these findings, we suggest that public libraries should offer book curation services that allow users to discover new information and select themes that are engaging and interesting, and provide information about various genres and types of books.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.97-106
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• 2014

The development of big data technology was to more accurately predict diversified contemporary society and to more efficiently operate it, and to enable impossible technique in the past. This technology can be utilized in various fields such as the social science, economics, politics, cultural sector, and science technology at the national level. It is a prerequisite to find valuable information by data mining techniques in order to analyze big data. Data mining techniques associated with big data involve text mining, opinion mining, cluster analysis, association rule mining, and so on. The most widely used data mining technique is to explore association rules. This technique has been used to find the relationship between each set of items based on the association thresholds such as support, confidence, lift, similarity measures, etc.This paper proposed cosine net confidence as association thresholds, and checked the conditions of interestingness measure proposed by Piatetsky-Shapiro, and examined various characteristics. The comparative studies with basic confidence and cosine similarity, and cosine net confidence were shown by numerical example. The results showed that cosine net confidence are better than basic confidence and cosine similarity because of the relevant direction.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.945-953
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• 2006

The researchers in the field of cognitive science and learning science suggest that the teaching activity induces the elaborative and meaningful learning. Actually, lots of research findings have shown the beneficial effect of learning by teaching such as peer tutoring. But peer tutoring has some limitations in the practical learning context. To overcome some limitations, the new concept of "learning by teaching" through the agent called Teachable Agent. The teachable agent is a modified version of traditional intelligent tutoring system that assigns a role of tutor to teach the agent. The teachable agent monitors individual difference and provides a student with a chance for deep learning and motivation to learn by allowing them to play an active role in the process of learning. That is, The teaching activity induces the elaborative and meaningful learning. This study compared the effects of our teachable agent, KORI, and peer tutoring on the cognition and motivation. The field experiment was conducted to examine whether learning by teaching the teachable agent would be more effective than peer tutoring and reading condition. In the experiment, all participants took 30 minutes lesson on rock and rock cycle together to acquire the base knowledge in the domain. After the lesson, participants were randomly assigned to one of the three experimental conditions; reading condition, peer tutoring condition, and teachable agent condition. Next, participants of each condition moved into separated place and performed their own learning activity. After finishing all of the learning activities in each condition, all participants were instructed to rate the interestingness using a 5-point scale on their own learning activity and leaning material, and were given the comprehension test. The results indicated that the teachable agent condition and the peer tutoring condition showed more interests in the learning than the reading condition. It is suggested that teachable agent has more advantages in overcoming the several practical limitations of peer tutoring such as restrictions in time and place, tutor's cognitive burden, unnecessary interaction during peer tutoring. The applicability and prospects of the teachable agent as an efficient substitute for peer tutoring and traditional intelligent tutoring system were also discussed.

• Journal of the Korean Data and Information Science Society
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• v.25 no.3
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• pp.601-609
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• 2014

The most widely used data mining technique for big data analysis is to generate meaningful association rules. This method has been used to find the relationship between set of items based on the association criteria such as support, confidence, lift, etc. Among them, confidence is the most frequently used, but it has the drawback that we can not know the direction of association by it. The attributably pure confidence was developed to compensate for this drawback, but the value was changed by the position of two item sets. In this paper, we propose four symmetrically and attributably pure confidence measures to compensate the shortcomings of confidence and the attributably pure confidence. And then we prove three conditions of interestingness measure by Piatetsky-Shapiro, and comparative studies with confidence, attributably pure confidence, and four symmetrically and attributably pure confidence measures are shown by numerical examples. The results show that the symmetrically and attributably pure confidence measures are better than confidence and the attributably pure confidence. Also the measure NSAPis found to be the best among these four symmetrically and attributably pure confidence measures.

• Information Systems Review
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• v.25 no.2
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• pp.205-227
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• 2023

Recently, Virtual Human(VH) has begun to appear in various media, not only on social media, but also in advertisements, music sources, and dramas. Virtual human has become a primary marketing tool for companies, but there also exist concerns when the companies do not disclose the identities of virtual humans. Accordingly, it is necessary to examine users' responses toward content that features virtual humans. This study aimed to examine how the exposure of virtual humans in the content affects users' perceptions. Therefore, the study defined the concept of 'VH Disclosure(VHD)', referring to the exposure of the virtual human's identity, and explored the impact of VH disclosure on attitude toward the ad (Hedonism, Utilitarianism, and Interestingness) and source credibility (Trustworthiness and Expertise). The study conducted an experimental survey with 302 respondents. Regardless of when the ad featured a VH or a human, the results showed that there was no significant difference between users' attitudes and source credibility. The results revealed that it was more effective to disclose the VH in social media feeds than directly reveal the VH's identity in the content. Therefore, this study utilizes a new concept of 'VH Disclosure(VHD)' to enhance the understanding of VH and contributes to establishing marketing strategies optimized for consumers in the creation of virtual human-related content.

• Journal of The Korean Association For Science Education
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• v.43 no.6
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• pp.495-507
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• 2023

Teachers often encounter challenges in supporting students with question generation and the development of investigation plans in sensemaking activities. A primary challenge stems from the ambiguity surrounding how students apply their conceptual understandings in this process. This study aims to explore how students apply their conceptual understandings to generate questions and design investigation processes in a sensemaking activity. Two types of student group activities were identified and examined for comparison: One focused on designing a process to achieve the goal of sensemaking, and the other focused on following the step-by-step scientific inquiry procedures. The design of investigation process in each group was concretized with epistemic criteria used for evaluating the designs. The students' use of conceptual understandings in discussions around each was then examined. The findings reveal three epistemic criteria employed in generating questions and designing investigation processes. First, the students examined the interestingness of natural phenomena, using their conceptual understandings of the structure and function of entities within natural phenomena to identify a target phenomenon. This process involved verifying their existing knowledge to determine the need for new understanding. The second criterion was the feasibility of investigating specific variables with the given resources. Here, the students relied on their conceptual understandings of the structure and function of entities corresponding to each variable to assess whether each variable could be investigated. The third epistemic criterion involved examining whether the factors of target phenomena expressed in everyday terms could be translated into observable variables capable of explaining the phenomena. Conceptual understandings related to the function of entities were used to translate everyday expressions into observable variables and vice versa. The students' conceptual understanding of a comprehensive mechanism was used to connect the elements of the phenomenon and use the elements as potential factors to explain the target phenomenon. In the case where the students focused on carrying out step-by-step procedures, data collection feasibility was the sole epistemic criterion guiding the design. This study contributes to elucidating how the process of a sensemaking activity can be developed in the science classroom and developing conceptual supports for designing sensemaking activities that align with students' perspectives.