• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.7
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• pp.768-774
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• 2016

This paper deals with a method to recommend the combination of items as a group according to similarity to handle application area such as fashion and cooking, while the previous methods recommend single item such as a book, music or movie. Collaborative filtering is a method to recommend an item selected by users with similar tendency based on similarity between users. In this paper, the proposed method generates a set of frequent items based on collaborative filtering and association rules and recommends a group by similarity between groups. To show the validity of the proposed method, experiments are performed with purchase data collected from e-commerce for four months.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.683-692
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• 2003

The rapid increase of the number of channels makes it hard to find wanted programs from TV. In recent years, the number of channels come up to hundreds with the digital TV arrival. So, it will drive us to the new way of watching TV. In this paper, we introduce a recommendation system for TV programs to overcome this difficulty. We model user profiles and design each module of the system, considering TV environment. Our system gathers basic information from people manually and then updates user profiles automatically by tracking viewing and usage history. As a result, our system recommends daily TV programs based on the changing interest of users. In this paper, we address the problems and solutions by describing our system and the experiment.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.379-389
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• 2023

According to the Korea Urban Air Mobility (K-UAM) Concept of Operation (ConOps), the Global Navigation Satellite System (GNSS) is recommended as the primary navigation system and the performance specification will be implemented considering the standard of Performance Based Navigation (PBN). However, by taking into account the characteristics of an urban environment and the concurrent operations of multiple UAM aircraft, the current PBN standards for civil aviation seem difficult to be directly applied to an UAM aircraft. Therefore, by referring to technical documents published in the literature, this paper examines the feasibility of applying the proposed performance requirements to K-UAM, which follows the recommendation of navigation performance requirements for K-UAM. In accordance with the UAM ConOps, the UAM aircraft is anticipated to maintain low altitude during approach and landing phases. Subsequently, the navigation performance degradation could occur in the urban environment, and the primary degradation factor is identified as multipath error. For this reason, to ensure the safety and reliability of the K-UAM aircraft, it is necessary to analyze the degree of performance degradation related to the urban environment and then propose an alternative aid to enhance the navigation performance. To this end, the aim of this paper is to model the multipath effects of the GNSS in an urban environment and to carry out the simulation studies using the real GNSS datasets. Finally, the initial navigation performance requirement is proposed based on the results of the numerical simulation for the K-UAM.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.23-31
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• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.1009-1019
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• 2017

Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.121-129
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• 2015

With the explosive growth of information about books, there is a growing number of customers who find it difficult to pick a book. Against the backdrop, the importance of a book recommendation system becomes greater, through which appropriate information about books could be offered then to encourage customers to buy a book in the end. However, existing recommendation systems based on the bibliographical information or user data reveal the reliability issue found in their recommendation results. This is why it is necessary to reflect semantic information extracted from the texts of a book's main body in a recommendation system. Accordingly, this paper suggests a method for extracting keywords from the main body of novels, as a preceding research, by using TF-IDF method as well as the text structure. To this end, the texts of 100 novels have been collected then to divide them into four structural elements of preface, dialogue, non-dialogue and closing. Then, the TF-IDF weight of each keyword has been calculated. The calculation results show that the extraction accuracy of keywords improves by 42.1% in performance when more weight is given to dialogue while including preface and closing instead of using just the main body.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.586-594
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• 2004

Because web documents become creation and disappearance rapidly, users require the recommend system that offers users to browse the web document conveniently and correctly. One largely untapped source of knowledge about large data collections is contained in the cumulative experiences of individuals finding useful information in the collection. Recommendation systems attempt to extract such useful information by capturing and mining one or more measures of the usefulness of the data. The existing Information Filtering system has the shortcoming that it must have user's profile. And Collaborative Filtering system has the shortcoming that users have to rate each web document first and in high-quantity, low-quality environments, users may cover only a tiny percentage of documents available. And dynamic recommendation system using the user browsing pattern also provides users with unrelated web documents. This paper classifies these web documents using the similarity between the web documents under the web document type and extracts the user browsing sequential pattern DB using the users' session information based on the web server log file. When user approaches the web document, the proposed Dynamic recommendation system recommends Top N-associated web documents set that has high similarity between current web document and other web documents and recommends set that has sequential specificity using the extracted informations and users' session information.