• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1260-1272
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• 2024

This paper tackles the prevalent challenges faced by existing tourism route recommendation methods, including data sparsity, cold start, and low accuracy. To address these issues, a novel intelligent tourism route recommendation method based on collaborative filtering is introduced. The proposed method incorporates a series of key steps. Firstly, it calculates the interest level of users by analyzing the item attribute rating values. By leveraging this information, the method can effectively capture the preferences and interests of users. Additionally, a user attribute rating matrix is constructed by extracting implicit user behavior preferences, providing a comprehensive understanding of user preferences. Recognizing that user interests can evolve over time, a weight function is introduced to account for the possibility of interest shifting during product use. This weight function enhances the accuracy of recommendations by adapting to the changing preferences of users, improving the overall quality of the suggested tourism routes. The results demonstrate the significant advantages of the approach. Specifically, the proposed method successfully alleviates the problem of data sparsity, enhances neighbor selection, and generates tourism route recommendations that exhibit higher accuracy compared to existing methods.

• Smart Media Journal
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• v.9 no.1
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• pp.45-50
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• 2020

With the development of deep learning, studies using artificial neural networks based on deep learning in recommendation systems are being actively conducted. Especially, the recommendation system based on RNN (Recurrent Neural Network) shows good performance because it considers the sequential characteristics of data. This study proposes a travel route recommendation system using GRU(Gated Recurrent Unit) and Session-based Parallel Mini-batch which are RNN-based algorithm. This study improved the recommendation performance through an ensemble of top1 and bpr(Bayesian personalized ranking) error functions. In addition, it was confirmed that the RNN-based recommendation system considering the sequential characteristics in the data makes a recommendation reflecting the meaning of the travel destination inherent in the travel route.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1346-1357
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• 2021

The drone technology, which is receiving a lot of attention due to the 4th industrial revolution, requires an Unmanned Aerial Vehicles'(UAVs) flight path search algorithm for automatic operation and driver assistance. Various studies related to flight path prediction and recommendation algorithms are being actively conducted, and many studies using the A-Star algorithm are typically performed. In this paper, we propose an Optimal 3D Flight Path Recommendation System for unmanned aerial vehicles. The proposed system was implemented and simulated in Unity 3D, and by indicating the meaning of the route using three different colors, such as planned route, the recommended route, and the current route were compared each other. And obstacle response experiments were conducted to cope with bad weather. It is expected that the proposed system will provide an improved user experience compared to the existing system through accurate and real-time adaptive path prediction in a 3D mixed reality environment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.78-85
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• 2011

In this paper, a research on multi-agent is carried out in order to develop a system that can provide drivers with real-time route recommendation by reflecting Dynamic Environment Information which acts as an agent in charge of Driver's trait, road condition and Route recommendation system. DEI is equivalent to number of n multi-agent and is an environment variable which is used in route recommendation system with optimal routes for drivers. Route recommendation system which reflects DEI can be considered as a new field of topic in multi-agent research. The representative research of Multi-agent, the Prey Pursuit Problem, was used to generate a fresh solution. In this thesis paper, you will be able to find the effort of indulging the lack of Prey Pursuit Problem,, which ignored practicality. Compared to the experiment, it was provided a real practical experiment applying the algorithm, the new Ant-Q method, plus a comparison between the strategies of the established direction vector was put into effect. Together with these methods, the increase of the efficiency was able to be proved.

• 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 the Korea Society of Computer and Information
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• v.27 no.8
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• pp.151-160
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• 2022

In this paper, we propose an algorithm that enables near-field delivery at a faster and lowest cost to meet the growing demand for delivery services. The algorithm proposed in this study involves subway passengers (shipper) in logistics movement as delivery sources. At this time, the passenger may select a delivery logistics matching subway route. And from the perspective of the service user, it is possible to select a delivery man whose route matches. At this time, the delivery source recommendation is carried out in a text similarity measurement method that combines TF-IDF&N-gram and BERT. Therefore, unlike the existing delivery system, two-way selection is supported in a man-to-man method between consumers and delivery man. Both cost minimization and delivery period reduction can be guaranteed in that passengers on board are involved in logistics movement. In addition, since special skills are not required in terms of transportation, it is also meaningful in that it can provide opportunities for economic participation to workers whose job positions have been reduced.

• Convergence Security Journal
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• v.19 no.2
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• pp.73-81
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• 2019

In this paper, we propose a user-dependent mid-point navigation system using a time weighted mid-point navigation algorithm and a user preference based mid-point neighborhood recommendation system. The proposed system consists of a mid-point navigation module for calculating an mid-point by applying a time weight of each user based on a departure point between users, and a search module for providing a search for a route to the calculated mid-point. In addition, based on the mid-point search result, it is possible to increase the utilization rate of users by including a place recommending function based on user's preference. Experimental results show that the proposed system can increase the efficiency of using by the user-dependent mid-point navigation and place recommendation function.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.115-118
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• 2023

본 논문에서는 지속되던 코로나-19 바이러스로 인한 일상의 제약이 점차 완화되는 추세 속에서 이전에 영위하지 못하던 개개인의 여가생활을 지원하기 위해 개발하였다. 제약이 완화되면서 많은 사람들이 국내 여행의사가 점차 증가된다고 분석된다. 지금 우리의 일상 속에는 인간이 직접 의사결정을 하는 부분들이 많이 줄어들었다. 공공데이터를 이용한 자동화된 경로 추천 시스템을 통해 사용자들은 의사결정의 단계 없이 제공되는 경로를 지도 API를 통해 시각적으로 이용하며 나들이 준비 과정을 간소화 시킬 것으로 예상된다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.211-221
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• 2024

Live streaming commerce has emerged as an innovative e-commerce model. This study, based on the Elaboration Likelihood Model (ELM), aims to explore the impact of Key Opinion Consumers' (KOCs) attributes in live streaming commerce on purchase intentions on short video platforms. A survey was conducted with 411 consumers, and data analysis and hypothesis testing were performed using SPSS 24.0 and AMOS 23.0 software. Research has found that differences in consumers' information processing abilities lead to different pathway selections. Central route factors such as recommendation consistency, product involvement, and professionalism, as well as peripheral route factors such as recommendation timeliness, all have significant positive effects on consumers' purchase intention. However, visual cues in the peripheral route do not have a significant impact. This study aims to provide theoretical support and practical guidance for the development of the live streaming commerce industry, and to help companies adjust their promotion strategies based on differences in consumer information processing, thereby improving purchase conversion rates.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.480-490
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• 2018

Recently, The MRV (monitoring, reporting and verification) regulation, which measures, reports and verifies the emission gas of vessel to head for member countries of Europe Union (EU), is being implemented. As part this reason, we develop a system that estimates simple fuel consumption and recommends optimized stop-over ports of vessel, to calculate amount of carbon emission. To do this, we analyze fuel, distance and time consumption between port and the other port based on stored voyage data for over 10 years of real-ship, and implement a simple fuel consumption estimation module using analyzed result. Also, we design and implement the optimized route recommendation algorithm, existing navigation route display function including comparison with the optimized routes and user custom route plan function. Therefore, we expect the developed system is helpful when makes a navigation route and so on by reference indexes and we anticipate the system to have a sense for future research which learns and predicts for accuracy result.