• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.

• Journal of Korean Library and Information Science Society
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• v.53 no.2
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• pp.95-115
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• 2022

The purpose of this study is to transform the sub-categorization terms of the National Science and Technology Standards Classification System into technical keywords by applying a machine learning algorithm. For this purpose, AttentionMeSH was used as a learning algorithm suitable for topic word recommendation. For source data, four-year research status files from 2017 to 2020, refined by the Korea Institute of Science and Technology Planning and Evaluation, were used. For learning, four attributes that well express the research content were used: task name, research goal, research abstract, and expected effect. As a result, it was confirmed that the result of MiF 0.6377 was derived when the threshold was 0.5. In order to utilize machine learning in actual work in the future and to secure technical keywords, it is expected that it will be necessary to establish a term management system and secure data of various attributes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.238-252
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• 2009

Due to the rapid increase of available contents via the convergence of broadcasting and internet, the efficient access to personally preferred contents has become an important issue. In this paper, for recommendation scheme for TV programs using a collaborative filtering technique is studied. For recommendation of user preferred TV programs, our proposed recommendation scheme consists of offline and online computation. About offline computation, we propose reasoning implicitly each user's preference in TV programs in terms of program contents, genres and channels, and propose clustering users based on each user's preferences in terms of genres and channels by dynamic fuzzy clustering method. After an active user logs in, to recommend TV programs to the user with high accuracy, the online computation includes pulling similar users to an active user by similarity measure based on the standard preference list of active user and filtering-out of the watched TV programs of the similar users, which do not exist in EPG and ranking of the remaining TV programs by proposed rank model. Especially, in this paper, the BM (Best Match) algorithm is extended to make the recommended TV programs be ranked by taking into account user's preferences. The experimental results show that the proposed scheme with the extended BM model yields 62.1% of prediction accuracy in top five recommendations for the TV watching history of 2,441 people.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2001.10a
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• pp.19-25
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• 2001

At present the identification of vessels is still depending on the OOW (Officer Of Wateh) in VTS (Vessel Traffic Service), which is completed by radar, and also by the combination of VHF radio and VHF direction finder. However, with the development of port transportation and economic, this conventional way of identification can't satisfy more and more request for the information that the VTS needs from the vessels. In such a case, the AIS(Automatic Identification System) precept which is based on STDMA (Self-organized Time Division Multiple Access) technique is put forward by IMO (International Maritime Organization). AIS can automatically provide the information, including own ship's identification, type, position, course, speed, and other information to the appropriately equipped coast station and other ships. At the same time it can also automatically monitor and track the nearby ships similarly fitted with AIS. On the basis of describing the whole comprising and the format of transmission information of AIS, this paper mainly studies the key communication techniques in AIS, such as STDMA protocol, net synchronization and GMSK(Gaussian Minimum Shift Keying)technique, and so on. At last this paper briefly introduces the recommendation decided by IMO on forcing the sea-going ships to fixed with AIS equipments, and it continuos with the unexploited potential of AIS if it applies in VTS.

• Journal of Distribution Science
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• v.18 no.4
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• pp.73-78
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• 2020

Purpose: This study was to examine public access defibrillator (PAD) deployment on some golf courses and to analyze automatic external defibrillators (AEDs) demand by appropriate distance. Research design, data, and methodology: We conducted telephone interview on 124 golf courses in Gyeonggi and Gangwon province in South Korea. The area within 3 minutes by 3 minutes for retrieval and 1 minute for shock and 1.5 minutes by the American Heart Association (AHA)recommendation for community AED placement were calculated as 3.14×162㎡ and 3.14×100㎡. Results: The average area was 1,811,481.8㎡, and 29 (42.7%) in below 999,999㎡, 75 (60.5%) in 1,000,000 to 1,999,999㎡, 12 (9.7%) in 2,000,000 to 2,999,999㎡ took up. The average retrieval time was 161.8 seconds, and 5 (4.1%) in below 90 seconds, 10 (8.0%) in 91 to 180 seconds took up a small part. AED demands according to 3 and 1.5 retrieval minutes were 2,602 and 6,986 respectively. Average AED demands per golf course were 21.0 and 56.3 respectively on 124 golf courses. Conclusions: The numbers of AED needed in South Korean golf course were 5,880 to 15,764. To ensure defibrillation on the golf courses, the supply and distribution of AEDs should be strengthened.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.3
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• pp.59-69
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• 2017

The best 'hub' to communicate with the citizen is using social media to marketing the business. However, there has several issued and the most common issue that face in critical is a capital issue. This issue is always highlight because most of automatic sentiment detection tool for Facebook or any other social media price is expensive and they lack of technical skills in order to control the tool. Therefore, in directly they have some obstacle to get faster product's feedback from customers. Thus, the personal online retailing need to struggle to stay in market because they need to compete with successful online company such as G-market. Sentiment analysis also known as opinion mining. Aim of this research is develop the tool that allow user to automatic detect the sentiment comment on social media account. RAD model methodology is chosen since its have several phases could produce more activities and output. Soppy tool will be develop using Microsoft Visual. In order to generate an accurate sentiment detection, the functionality testing will be use to find the effectiveness of this Soppy tool. This proposed automated Soppy Tool would be able to provide a platform to measure the impact of the customer sentiment over the postings on their social media site. The results and findings from the impact measurement could then be use as a recommendation in the developing or reviewing to enhance the capability and the profit to their personal online retailing company.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.180-191
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• 2005

The rapid growth of mobile communication technology has provided the expansion of mobile internet services, particularly mobile commerce takes much weight among them. Even though current mobile commerce service has serious problems which check its development, such as limited contents, expensive charge system and hardware restriction of mobile device, it is strongly expected as one of the next generation Internet services. In this paper, we summarize the problems like above and provide some total solution to meet them as follows: a function for automatic gathering of product information on online Internet and automatic translation it to data for mobile commerce, a middlelet application which provides functions for product search and order on the mobile device through off-line processing, and a function of user adaptive recommendation. We have actually designed and implemented the proposed system and verified the functions and effectiveness of the system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.21-28
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• 2024

Nowadays, based on the 4th Industrial Revolution, by using the CHATGPT function and 3D virtual reality technology, anyone can easily open a virtual environment WEB-based, smart tourism OPEN source and travel destination without having to directly visit the travel location in the real world. Using the API function, it provides the convenience of virtual tourism. However, this function does not work if the travel transportation system is suddenly changed due to sudden bad weather, travel operation information cannot be checked in real time, and due to a lack of flight cancellation information and passenger ship operation information, it cannot be used until the plane or ferry departs normally. A very inconvenient problem arises where you have to wait a long time in the waiting room. Therefore, in this paper, in order to solve this problem, automatic duty-free product information and automatic product payment functions were added even when passenger ship cancellations and operation information suddenly occur due to bad weather and multiple products are purchased during the trip. In addition, the computer simulation experiment was conducted on a WEB basis so that anyone can conveniently travel smartly.

• Management & Information Systems Review
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• v.36 no.4
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• pp.129-148
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• 2017

Conventional exhibition management information systems recommend tourist attractions that are close to the place in which an exhibition is held. Some recommended attractions by the location-based recommendation could be meaningless when nothing is related to the exhibition's topic. Our goal is to recommend attractions that are related to the content presented in the exhibition, which can be coined as content-based recommendation. Even though human exhibition curators can do this, the quality is limited to their manual task and knowledge. We propose an automatic way of discovering attractions relevant to an exhibition of interests. Language resources are incorporated to discover attractions that are more meaningful. Because a typical single machine is unable to deal with such large-scale language resources efficiently, we implemented the algorithm on top of Apache Spark, which is a well-known distributed computing framework. As a user interface prototype, a web-based system is implemented that provides users with a list of relevant attractions when users are browsing exhibition information, available at http://bike.snu.ac.kr/WARP. We carried out a case study based on Korean Tourism Organization Linked Open Data with Korean Wikipedia as a language resource. Experimental results are demonstrated to show the efficiency and effectiveness of the proposed system. The effectiveness was evaluated against well-known exhibitions. It is expected that the proposed approach will contribute to the development of both exhibition and tourist industries by motivating exhibition visitors to become active tourists.