• KIISE Transactions on Computing Practices
• /
• v.21 no.2
• /
• pp.101-108
• /
• 2015

This paper presents the implementation of a system that recommends empirical best routes based on classification of large trajectory data. As many location-based services are used, we expect the amount of location and trajectory data to become big data. Then, we believe we can extract the best empirical routes from the large trajectory repositories. Large trajectory data is clustered into similar route groups using Hadoop MapReduce framework. Clustered route groups are stored and managed by a DBMS, and thus it supports rapid response to the end-users' request. We aim to find the best routes based on collected real data, not the ideal shortest path on maps. We have implemented 1) an Android application that collects trajectories from users, 2) Apache Hadoop MapReduce program that can cluster large trajectory data, 3) a service application to query start-destination from a web server and to display the recommended routes on mobile phones. We validated our approach using real data we collected for five days and have compared the results with commercial navigation systems. Experimental results show that the empirical best route is better than routes recommended by commercial navigation systems.

• The KIPS Transactions:PartD
• /
• v.16D no.3
• /
• pp.451-462
• /
• 2009

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information. However, despite its success and popularity, CF encounters a serious limitation with quality evaluation, called cold start problems. To alleviate this limitation, in this paper, we propose a unique method of building models derived from explicit ratings and applying the models to CF recommender systems. The proposed method is divided into two phases, an offline phase and an online phase. First, the offline phase is a building pre-computed model phase in which most of tasks can be conducted. Second, the online phase is either a prediction or recommendation phase in which the models are used. In a model building phase, we first determine a priori predicted rating and subsequently identify prediction errors for each user. From this error information, an error-reflected model is constructed. The error-reflected model, which is reflected average prior prediction errors of user neighbors and item neighbors, can make accurate predictions in the situation where users or items have few opinions; this is known as the cold start problems. In addition, in order to reduce the re-building tasks, the error-reflected model is designed such that the model is updated effectively and users'new opinions are reflected incrementally, even when users present a new rating feedback.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.129-164
• /
• 2023

In a situation where the use and introduction of Large Language Models (LLMs) is expanding due to recent developments in generative AI technology, it is difficult to find actual application cases or implementation methods for the use of internal company data in existing studies. Accordingly, this study presents a method of implementing generative AI services using the LLM application architecture using the most widely used LangChain framework. To this end, we reviewed various ways to overcome the problem of lack of information, focusing on the use of LLM, and presented specific solutions. To this end, we analyze methods of fine-tuning or direct use of document information and look in detail at the main steps of information storage and retrieval methods using the retrieval augmented generation (RAG) model to solve these problems. In particular, similar context recommendation and Question-Answering (QA) systems were utilized as a method to store and search information in a vector store using the RAG model. In addition, the specific operation method, major implementation steps and cases, including implementation source and user interface were presented to enhance understanding of generative AI technology. This has meaning and value in enabling LLM to be actively utilized in implementing services within companies.

• Information Systems Review
• /
• v.24 no.1
• /
• pp.1-19
• /
• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Information Systems Review
• /
• v.22 no.2
• /
• pp.25-41
• /
• 2020

Along with the expectation about 5G network commercialization, interests in realistic and immersive media industries such as virtual reality (VR) are increasing. However, most of studies on VR still focus on video technologies instead of factors for popularity and consumption. Thus, the main objective of this research is to identify meaningful factors, which affect the view counts of VR videos and to provide business implications of the content strategies for VR video creators and service providers. Using a regression analysis with 700 VR videos, this study tries to find major factors that affect the view counts of VR videos. As a result, user assessment factors such as number of likes and sicknesses have a strong influence on the view counts. In addition, the result shows that both general information factors (video length and age) and content characteristic factors (series, one source multi use (OSMU), and category) are all influential factors. The findings suggest that it is necessary to support recommendation and curation based on user assessments for increasing popularity and diffusion of VR video streaming.

• Information Systems Review
• /
• v.20 no.1
• /
• pp.1-16
• /
• 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 the korean academy of Pediatric Dentistry
• /
• v.31 no.4
• /
• pp.636-644
• /
• 2004

Recently, self-etching adhesive system have been developed and bonding procedures simplified into one or two steps, which are simultaneously applied to both enamel and dentin. These systems are easy to use and have the potential for good clinical success. The purpose of this study is to evaluate in vitro the microleakage on the cementum/dentin and enamel walls in composite resin restoration of Class V cavities, regarding the use of different adhesive systems. 30 human premolars were divided into 3 groups. A standardized Class V preparation was prepared on the buccal and lingual surface of each premolar. The preparation were made parallel to the cementoenamel junctions, with the gingival half of the preparation extending 1mm apical to the cementoenamel junction. After adhesive system was applied to teeth as manufacture's recommendation, hybrid resin composite was filled in bulk into the preparation and light polymerized according to manufacturer's recommendations. Specimen were stored in distilled water at $37^{\circ}C$ for 5 days and thermocycled 1000 times ($5^{\circ}C{\pm}2^{\circ}C\;and\;55^{\circ}C{\pm}2^{\circ}C)$, then immersed in a 2% methylene blue solution for 12 hours. After sectioning mesio distally through the restorations, the degree of dye penetration was scored under a stereomicroscope at ${\times}\;25$ magnification. The data were analyzed statistically using t-test and one-way ANOVA. The results were as follows: ${\cdot}$ There is no adhesive system which can prevent microleakage perfectly. ${\cdot}$ There is significant difference in microleakage between enamel margin and dentin margin (p<0.0001). ${\cdot}$ In enamel margin, self-etching primer systems did not show any significant difference comparing total-etching system. In denin margin, self-etching primer systems did not show any significant difference comparing one-bottle adhesive system used in combination with total-etching.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.1-31
• /
• 2017

Modern businesses are adopting new technologies to serve their markets better as well as to improve efficiency and productivity. The advertising industry has continuously experienced disruptions from the traditional channels (radio, television and print media) to new complex ones including internet, social media and mobile-based advertising. This case study focuses on proposing intelligent advertising business model in Seoul's metro network. Seoul has one of the world's busiest metro network and transports a huge number of travelers on a daily basis. The high number of travelers coupled with a well-planned metro network creates a platform where marketers can initiate engagement and interact with both customers and potential customers. In the current advertising model, advertising is on illuminated and framed posters in the stations and in-car, non-illuminated posters, and digital screens that show scheduled arrivals and departures of metros. Some stations have digital screens that show adverts but they do not have location capability. Most of the current advertising media have one key limitation: space. For posters whether illuminated or not, one space can host only one advert at a time. Empirical literatures show that there is room for improving this advertising model and eliminate the space limitation by replacing the poster adverts with digital advertising platform. This new model will not only be digital, but will also provide intelligent advertising platform that is driven by data. The digital platform will incorporate location sensing, e-commerce, and mobile platform to create new value to all stakeholders. Travel cards used in the metro will be registered and the card scanners will have a capability to capture traveler's data when travelers tap their cards. This data once analyzed will make it possible to identify different customer groups. Advertisers and marketers will then be able to target specific customer groups, customize adverts based on the targeted consumer group, and offer a wide variety of advertising formats. Format includes video, cinemagraphs, moving pictures, and animation. Different advert formats create different emotions in the customer's mind and the goal should be to use format or combination of formats that arouse the expected emotion and lead to an engagement. Combination of different formats will be more effective and this can only work in a digital platform. Adverts will be location based, ensuring that adverts will show more frequently when the metro is near the premises of an advertiser. The advertising platform will automatically detect the next station and screens inside the metro will prioritize adverts in the station where the metro will be stopping. In the mobile platform, customers who opt to receive notifications will receive them when they approach the business premises of advertiser. The mobile platform will have indoor navigation for the underground shopping malls that will allow customers to search for facilities within the mall, products they may want to buy as well as deals going on in the underground mall. To create an end-to-end solution, the mobile solution will have a capability to allow customers purchase products through their phones, get coupons for deals, and review products and shops where they have bought a product. The indoor navigation will host intelligent mobile-based advertisement and a recommendation system. The indoor navigation will have adverts such that when a customer is searching for information, the recommendation system shows adverts that are near the place traveler is searching or in the direction that the traveler is moving. These adverts will be linked to the e-commerce platform such that if a customer clicks on an advert, it leads them to the product description page. The whole system will have multi-language as well as text-to-speech capability such that both locals and tourists have no language barrier. The implications of implementing this model are varied including support for small and medium businesses operating in the underground malls, improved customer experience, new job opportunities, additional revenue to business model operator, and flexibility in advertising. The new value created will benefit all the stakeholders.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.111-126
• /
• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Korean Society of Environmental Engineers
• /
• v.38 no.2
• /
• pp.56-62
• /
• 2016

The purpose of this research is to explore both on-site manager's and resident's assessment of the Automated Vacuum Waste Collection System (AVWCS) in suburban apartment complexes. In doing so, a self-administered questionnaire survey was conducted in 10 districts and their 11 apartment complexes in the Seoul Metropolitan Area. The main findings can be summarized as follows: the managers considered the AVWCS to be economically efficient and environmentally fiendly, and suggested that the system be managed in a more professional way, with an advanced technology and by more qualified technicians. The recommendation was related to residents' complaints and frequent mechanical failures frequently occurring in waste inlets and waste transport piping of the system. For residents using the system, the system was satisfactory, and should be necessarily improved with more user-friendly features. Further, most comments made by the residents were relevant to waste inlets such as safety, cleanliness, prompt repair, odor reduction, waste separation. It's of significant to train residents with how to properly use the system, which is expected to substantially fall a number of residents' complaints. Therefore, both professional management of AVWCS and regular workshops on how to utilize it are crucial in order to heighten its strengths.