• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.561-571
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• 2020

Location information service using big data is continuously developing. In terms of navigation, the range of services from map API service to ship navigation information has been expanded, and system application information has been extended to SNS and blog search records for each location. Recently, it is being used as a new industry such as location-based search and advertisement, driverless cars, Internet of Things (IoT) and online to offline (O2O) services. In this study, we propose an information system that enables users to receive information about nearby stores more effectively by using big data when a user moves a specific route. In addition, we have designed this system so that local stores can use this system to effectively promote it at low cost. In particular, we analyzed web-based information in real time to improve the accuracy of information provided to users by complementing the data. Through this system, system users will be able to utilize the information more effectively. Also, from a system perspective, it can be used to create new services by integrating with various web services.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.504-507
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• 2003

These days, XML is accepted and used to e-commerce market broadly. But by reason of XML document has autonomy of expression that can exist same form logically but several other forms physically, several problems ran happen in application that judge effectiveness as physical form such as XML digital signature. Therefore, it is recommending to propose and use Canonical XML algorithm to change identical XML document physically equally logically in W3C to solve this problems. We implemented system that run Canonical XML algorithm that suggested in W3C that can change to more elaborate regular document. Thus, interpretable with other application that takes W3C recommendation. Also, as well as use in digital signature system for web service is useful, use in several system that physical identify is required when it exchanges XML document for web service interoperability are considered to be valuable. Moreover, Adding the transformation ability between universal encoding scheme and EUC-KR that is internal encoding scheme should be Canonical XML Algorithm that is suited to internal circumstances, and this should be a foundation technique of international interoperability confirmedness.

• 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 Society for e-Business Studies
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• v.15 no.4
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• pp.181-195
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• 2010

The participation and share between personal users are the driving force of Web 2.0, and easily found in blog, social network, collective intelligence, social bookmarking and tagging. Among those applications, the social bookmarking lets Internet users to store bookmarks online and share them, and provides various services based on shared bookmarks which people think important.Delicious.com is the representative site of social bookmarking services, and provides a bookmark search service by using tags which users attach to the bookmarks. Our paper suggests a method re-ranking the ranks from Delicious.com based on user tags in order to provide personalized bookmark recommendations. Moreover, a method to consider bookmarks which have tags not directly related to the user query keywords is suggested by using tag network based on Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare the ranks by Delicious.com with new ranks of our system.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.117-135
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• 2003

This study is to propose a procedure solving scale problem of traditional collaborative filtering (CF) approach. The CF approach generally uses some similarity measures like correlation coefficient. So, as the user of the Website increases, the complexity of computation increases exponentially. To solve the scale problem, this study suggests a clustering model-based approach using Self-Organizing Map (SOM) and RFM (Recency, Frequency, Momentary) method. SOM clusters users into some user groups. The preference score of each item in a group is computed using RFM method. The items are sorted and stored in their preference score order. If an active user logins in the system, SOM determines a user group according to the user's characteristics. And the system recommends items to the user using the stored information for the group. If the user evaluates the recommended items, the system determines whether it will be updated or not. Experimental results applied to MovieLens dataset show that the proposed method outperforms than the traditional CF method comparatively in the recommendation performance and the computation complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1698-1707
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• 2003

These days, XML is accepted and used to e commerce market broadly. But by reason of XML document can exist same form logically but several other forms physically, several problems can happen in application that judge effectiveness as physical form such as XML digital signature. Therefore, it is recommending to propose and use canonical XML algorithm to change identical XML document physically equally logically in W3C to solve this problems. We implemented system that nm Canonical XML algorithm that suggested in W3C that can change to mon elaborate regular document. Thus, interoperable with other application that takes W3C recommendation Also, as well as use in digital signature system for web service is useful, use in several system that physical identify is required when it exchanges na document for web service interoperability are considered to be valuable. Moreover, Adding the transformation ability between universal encoding scheme and EUC­KR that is internal encoding scheme should be Canonical XML Algorithm that is suited to internal circumstances, and this should be a foundation technique of international interoperability confirmedness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.89-96
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• 2021

The number of confirmed cases of coronavirus-19 is soaring around the world and has caused numerous deaths. Wearing a mask is very important to prevent infection. Incidents and accidents have occurred due to the recommendation to wear a mask in public places such as buses and subways, and it has emerged as a serious social problem. To solve this problem, this paper proposes an open source-based face mask wearing monitoring system. We used open source software, web-based artificial intelligence tool teachable machine and open source hardware Arduino. It judges whether the mask is worn, and performs commands such as guidance messages and alarms. The learning parameters of the teachable machine were learned with the optimal values of 50 learning times, 32 batch sizes, and 0.001 learning rate, resulting in an accuracy of 1 and a learning error of 0.003. We designed and implemented a mask wearing monitoring system that can perform commands such as guidance messages and alarms by determining whether to wear a mask using a web-based artificial intelligence tool teachable machine and Arduino to prove its validity.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1911-1916
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• 2013

One significant challenge that arises in social tagging systems is the rapid increase in the number and diversity of the tags. As opposed to structured annotation systems, tags provide users an unstructured, open-ended mechanism to annotate and organize web-content. In this paper, we propose a scheme for URL recommendation that is based on a folksonomy which is comprised of user-defined tags, URL-keywords and the category folder name as the major element. This scheme will be further improved and implemented on a browser extension that recommends to users the best way to classify a particular URL.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.111-121
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• 2009

In this paper, we propose a multimedia recommender system using user's playback time. Proposed system collects multimedia content which is requested by user and its user‘s playback time, as web log data. The system predicts playback time.based preference level and related contents from collected transaction database by fuzzy association rule mining. Proposed method has a merit which sorts recommendation list according to preference without user’s custom preference data, and prevents a false preference. As an experimental result, we confirm that proposed system discovers useful rules and applies them to recommender system from a transaction which doesn‘t include custom preferences.