• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.205-213
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• 2010

This study uses the FP-tree algorithm, one of the mining techniques. This study is an attempt to suggest a new recommended system using a modified FP-tree algorithm which yields an association rule based on frequent 2-itemsets extracted from the transaction database. The modified recommended system consists of a pre-processing module, a learning module, a recommendation module and an evaluation module. The study first makes an assessment of the modified recommended system with respect to the precision rate, recall rate, F-measure, success rate, and recommending time. Then, the efficiency of the system is compared against other recommended systems utilizing the sequential pattern mining. When compared with other recommended systems utilizing the sequential pattern mining, the modified recommended system exhibits 5 times more efficiency in learning, and 20% improvement in the recommending capacity. This result proves that the modified system has more validity than recommended systems utilizing the sequential pattern mining.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.803-813
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• 2013

The purchase of items in e-commerce is a little bit different from that of items in off-line. The recommendation of items in off-line is conducted by salespersons' recommendation, However, the item recommendation in e-commerce cannot be recommended by salespersons, and so different types of methods can be recommended in e-commerce. Recommender system is a method which recommends items in e-commerce. Preferences of customers who want to purchase new items can be predicted by the preferences of customers purchasing existing items. In the recommender system, the items with estimated high preferences can be recommended to customers. The algorithm of collaborative filtering is used in recommender system of e-commerce, and the list of recommended items is made by estimated values, and then the list is recommended to customers. The dataset used in this research are 100k dataset and 1 million dataset in Movielens dataset. Similar results in two dataset are deducted for generalization. To suggest a new algorithm, distribution features of estimated values are analyzed by the existing algorithm and transformed algorithm. In addition, respondent'distribution features are analyzed respectively. To improve the collaborative filtering algorithm in neighborhood recommender system, a new algorithm method is suggested on the basis of existing algorithm and transformed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2636-2640
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• 2003

KARI had developed an Local Area Augmentation System for aircraft precision landing as following ICAO SARPs(Standards and Recommended Practices) draft and FAA's recommended algorithm( carrier smoothing techniques). JPO in charge of managing GPS has introduced the signal structure of GPS modernization program. This paper estimates the accuracy performance of KARI-LAAS with modernized GPS signal but the same processing algorithm.

• Nuclear Engineering and Technology
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• v.32 no.6
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• pp.537-548
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• 2000

The requirement of ultra high reliability of the safety critical software can not be demonstrated by testing alone. The specification based on formal method is recommended for safety system software. But there exist various kinds of formal methods, and this variety of formal method is recognized as an obstacle to the wide use of formal method. In this paper six different formal method have been applied to the same part of the functional requirements that is calculation algorithm intensive. The specification results were compared against the criteria that is derived from the characteristics that good software requirements specifications should have and regulatory body recommends to have. The application experience shows that the critical characteristics should be defined first, then appropriate method has to be selected. In our case, the Software Cost Reduction method was recommended for internal condition or calculation algorithm checking, and statechart method is recommended for the external behavioral description.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.221-232
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• 2023

In this paper, we propose a recommendation system based on the latent factor model using matrix factorization, which is one of the most commonly used collaborative filtering algorithms for recommendation systems. In particular, by introducing the concept of creating a list of recommended content and a list of non-preferred recommended content, and removing the non-preferred recommended content from the list of recommended content, we propose a method to ultimately increase the satisfaction. The experiment confirmed that using a separate list of non-preferred content to find non-preferred content increased precision by 135%, accuracy by 149%, and F1 score by 72% compared to using the existing recommendation list. In addition, assuming that users do not view non-preferred content through the proposed algorithm, the average evaluation score of a specific user used in the experiment increased by about 35%, from 2.55 to 3.44, thereby increasing user satisfaction. It has been confirmed that this algorithm is more effective than the algorithms used in existing recommendation systems.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.109-112
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• 2000

The EPRCA(Enhanced Proportional Rate Control Algorithm) is one of the explicit rate control algorithm recommended by the ATM forum. EPRCA algorithm is using only two threshold to buffer change is not considered for calculating ER value. In this paper to cover the shortage, two more variables are added for considering increase of buffer quantity. So calculating proper ER would avoid before getting into the congestion situation. To analysis the affect, EPRCA and proposed algorithm are compared through simulation by analysis of change of buffer length and ACR change

• Journal of Korea Multimedia Society
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• v.22 no.9
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• pp.1108-1121
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• 2019

As the quality and level of life rise, many people are doing search for various pieces of information about tourism. In addition, users prefer the search methods reflecting individual opinions such as SNS and blogs to the official websites of tourist destination. Many of previous studies focused on a recommendation system for tourist courses based on the GPS information and past travel records of users, but such a system was not capable of recommending the latest tourist trends. This study thus set out to collect and analyze the latest SNS data to recommend tourist destination of high interest among users. It also aimed to propose an altered TSP algorithm to recommend the optimal routes to the recommended destination within an area and a system to recommend the optimal tourist courses by applying the Elasticsearch engine. The altered TSP algorithm proposed in the study used the location information of users instead of Dijkstra's algorithm technique used in previous studies to select a certain tourist destination and allowed users to check the recommended courses for the entire tourist destination within an area, thus offering more diverse tourist destination recommendations than previous studies.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.1-10
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• 2021

This study empirically confirmed 'the political bias of the YouTube recommendation algorithm' and 'the selective exposure of user' to verify the Filter Bubble phenomenon of YouTube. For the experiment, two new YouTube accounts were opened and each account was trained simultaneously in a conservative and a liberal account for a week, and the "Recommended" videos were collected from each account every two days. Subsequently, through the text mining method, the goal of the research was to investigate whether conservative videos are more recommended in a righties account or lefties videos are more recommended in a lefties account. And then, this study examined if users who consumed political news videos via YouTube showed "selective exposure" received selected information according to their political orientation through a survey. As a result of the Text Mining, conservative videos are more recommended in the righties account, and liberal videos are more recommended in the lefties account. Additionally, most of the videos recommended in the righties/lefties account dealt with politically biased topics, and the topics covered in each account showed markedly definitive differences. And about 77% of the respondents showed selective exposure.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.852-854
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• 2005

This paper proposes a new Interior Point Method algorithm to improve the computation speed and solution stability, which have been challenging problems for employing the nonlinear Optimal Power Flow. The proposed algorithm is different from the traditional Interior Point Methods in that it adopts the Predictor-Corrector Method. It also accommodates the five minute dispatch, which is highly recommended in modern electricity market. Finally, the efficiency and applicability of the proposed algorithm is demonstrated with a case study.