• Title/Summary/Keyword: Recommending system

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GAN-based Automated Generation of Web Page Metadata for Search Engine Optimization (검색엔진 최적화를 위한 GAN 기반 웹사이트 메타데이터 자동 생성)

  • An, Sojung;Lee, O-jun;Lee, Jung-Hyeon;Jung, Jason J.;Yong, Hwan-Sung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.79-82
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    • 2019
  • This study aims to design and implement automated SEO tools that has applied the artificial intelligence techniques for search engine optimization (SEO; Search Engine Optimization). Traditional Search Engine Optimization (SEO) on-page optimization show limitations that rely only on knowledge of webpage administrators. Thereby, this paper proposes the metadata generation system. It introduces three approaches for recommending metadata; i) Downloading the metadata which is the top of webpage ii) Generating terms which is high relevance by using bi-directional Long Short Term Memory (LSTM) based on attention; iii) Learning through the Generative Adversarial Network (GAN) to enhance overall performance. It is expected to be useful as an optimizing tool that can be evaluated and improve the online marketing processes.

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Weather Data-Based Coordination Recommendation Smart Wardrobe System (날씨 데이터 기반 코디추천 스마트옷장 시스템)

  • Lee, Tae-Hun;Jeong, Hui;Kwon, Jang-Ryong;Baek, Pil-Gyu;Lee, Boong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.4
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    • pp.729-738
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    • 2022
  • Existing wardrobes have been used only for storing simple clothes. Since it has a function to store clothes, there is only one way to control the environment such as humidity or temperature, and there is only one way to purchase and store items such as a desiccant. In this paper, by increasing the convenience in the existing wardrobe, automatic temperature and humidity control and various convenient functions were added. In line with the smart home market and smart phone application market that have grown over the past several years, along with the development of a wardrobe with sensors, the temperature and humidity control function and other functions inside the wardrobe through Bluetooth pairing between the wardrobe and the smartphone can be customized to the user using a smartphone. Through the clothing selection function and the weather data in the application, we want to implement convenient functions such as the function of recommending clothes in the closet to match the weather.

Applications to Recommend Moving Route by Schedule Using the Route Search System of Map API (지도 API의 경로 탐색 시스템을 활용한 일정 별 동선 추천 애플리케이션)

  • Ji-Woo Kim;Jung-Yi Kim
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.23 no.2
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    • pp.1-6
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    • 2023
  • The purpose of this study is to research and develop so that users who are gradually progressing in the popularization of smartphones and the calculation of agricultural quality can use more active and flexible applications than existing application fields. People use event management applications to remember what they need to do, and maps applications to get to their appointments on time. You will need to build a glue-delivered application that leverages the Maps API to be able to recommend the glove's path for events so that the user can use the application temporarily. By comparing and analyzing currently used calendar, map, and schedule applications, several Open Maps APIs were compared to supplement the weaknesses and develop applications that converge the strengths. The results of application development by applying the optimal algorithm for recommending traffic routes according to time and place for the schedule registered by the user are described.

Factors Influencing the Intention of Traffic Accident Patients to Revisit and Recommend the Korean Medicine Clinics (교통사고 환자의 한방의료기관 재방문 및 추천의사에 영향을 미치는 요인)

  • Jae-Woo, Kim;Sung-Ho, Kim;Jung-Kyu, Kang
    • Journal of Society of Preventive Korean Medicine
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    • v.26 no.3
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    • pp.49-58
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    • 2022
  • Objectives : The purpose of this study is to analyze the factors affecting the intention of traffic accident patients, who had visited Korean medicine clinics for the purpose of treating traffic accidents, on revisiting and recommending those clinics to others. Methods : This study conducted the frequency analysis, Rao-scott chi-square test, and logistic regression analysis on 389 people, who answered that they had once visited Korean medicine clinics for treatment in traffic accidents, using data from the 2020 Korean Medicine Utilization and Herbal Medicine Consumption Survey. Results : As a result of the analyses, it was revealed that the significant influencing factors entailed marital status, job status, the attitude of medical staff, and access to the Korean medicine clinics, while only access to the Korean medicine clinics was a significant influencing factor for the intention to recommend to others. Especially, the intention of to revisit and to recommend in case of satisfying access to the Korean medicine clinics were 8.476 times and 6.784 times higher than when it is not the case. Conclusions : The results of this study reflect the characteristics of automobile insurance, and indicate that both further study and policy establishment on the operation of the automobile insurance system are required to ensure sufficient treatment for traffic accident patients.

Development of Flavouring Ontology for Recommending the Halal Status of Flavours

  • Siti Farhana Mohamad Hashim;Shahrul Azman Mohd Noah;Juhana Salim;Wan Aida Wan Mustapha
    • Journal of Information Science Theory and Practice
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    • v.12 no.2
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    • pp.22-35
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    • 2024
  • There has been a growing interest in halal-related ontology research in recent years, as ontology has gained recognition in the halal industry. This paper discusses the development of a flavouring ontology that will assist halal management auditors in predicting the halal status of flavours in order to process food producers' applications for halal certification. The development of a flavouring ontology is based on multiple references, because the auditors of halal management divisions must consult a variety of sources independently in order to determine the halal status of flavourings. The process includes 1) determining the ontology goal and scope, 2) building ontologies, and 3) evaluating the ontologies. The researcher used Protégé to design the ontologies, and Phyton was used to develop a prototype based on flavouring ontology. The developed ontology consists of four classes, nine sub-classes, and 11 relationships. The evaluation of the ontology using the prototype revealed that the majority of experts were satisfied with the information generated by the ontology in the prototype, particularly in relation to synonyms and the hierarchical structure of a flavour. However, the experts suggest improvements in terms of flavour metadata, especially on raw materials and natural occurrence data, so that the flavour information retrieved is comprehensive and accurate.

The Effects of Content and Distribution of Recommended Items on User Satisfaction: Focus on YouTube

  • Janghun Jeong;Kwonsang Sohn;Ohbyung Kwon
    • Asia pacific journal of information systems
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    • v.29 no.4
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    • pp.856-874
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    • 2019
  • The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.

Control of Several Fungi in the Recirculating Hydroponic System by Modified Slow Sand Filtration (재순환 양액재배시 저속 모래여과기 시스템을 이용한 진균류 제어)

  • Park, K.W.;Lee, G.P.;Kim, M.S.;Lee, S.J.;Seo, M.W.
    • Horticultural Science & Technology
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    • v.16 no.3
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    • pp.347-349
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    • 1998
  • Slow sand filtration was modified and applied for the determination of eliminating efficacy of various fungi and for recommending an easy approach to growers. After 1,500 liter filtration, Fusarium oxysporum was eliminated by several substrates such as activated charcoal (92.5% elimination), silica (90.8%), vermiculite (90.5%), sand (82.3%), perlite (50.4%), and hydroball (21.2%). Silica was able to eliminate several fungi by maximal ratio, which was corresponded to Fusarium oxysporum 120 cfu/mL. Collectotrichum lagenarium 98 cfu/mL. Phytophthora capsici 82 cfu/mL, Botrytis cinerea 62 cfu/mL, Pythium spp. 42 cfu/mL, and Sclerotinia ssp. 52 cfu/mL. In this case, the change of EC was minor and pH was maintained to about 7. In deep flow culture of 'Ddooksum Cheokchookmyeon' lettuce and 'Seokwang' tomato, silica-, activated charcoal-, and vermiculite-based filtration system successfully eliminated Fusarium oxysporum and Phytophthora capsici from the nutrient solution. As a result, these plants were not diseased by ten weeks after inoculation. With this system, growers can easily control the root-zone fungi in the recirculating hydroponic system.

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Contents Recommendation Search System using Personalized Profile on Semantic Web (시맨틱 웹에서 개인화 프로파일을 이용한 콘텐츠 추천 검색 시스템)

  • Song, Chang-Woo;Kim, Jong-Hun;Chung, Kyung-Yong;Ryu, Joong-Kyung;Lee, Jung-Hyun
    • The Journal of the Korea Contents Association
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    • v.8 no.1
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    • pp.318-327
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    • 2008
  • With the advance of information technologies and the spread of Internet use, the volume of usable information is increasing explosively. A content recommendation system provides the services of filtering out information that users do not want and recommending useful information. Existing recommendation systems analyze the records and patterns of Web connection and information demanded by users through data mining techniques and provide contents from the service provider's viewpoint. Because it is hard to express information on the users' side such as users' preference and lifestyle, only limited services can be provided. The semantic Web technology can define meaningful relations among data so that information can be collected, processed and applied according to purpose for all objects including images and documents. The present study proposes a content recommendation search system that can update and reflect personalized profiles dynamically in semantic Web environment. A personalized profile is composed of Collector that contains the characteristics of the profile, Aggregator that collects profile data from various collectors, and Resolver that interprets profile collectors specific to profile characteristic. The personalized module helps the content recommendation server make regular synchronization with the personalized profile. Choosing music as a recommended content, we conduct an experience on whether the personalized profile delivers the content to the content recommendation server according to a service scenario and the server provides a recommendation list reflecting the user's preference and lifestyle.

A Multimedia Contents Recommendation System using Preference Transition Probability (선호도 전이 확률을 이용한 멀티미디어 컨텐츠 추천 시스템)

  • Park, Sung-Joon;Kang, Sang-Gil;Kim, Young-Kuk
    • Journal of the Korean Institute of Intelligent Systems
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    • v.16 no.2
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    • pp.164-171
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    • 2006
  • Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

A Self-Service Business Intelligence System for Recommending New Crops (재배 작물 추천을 위한 셀프서비스 비즈니스 인텔리전스 시스템)

  • Kim, Sam-Keun;Kim, Kwang-Chae;Kim, Hyeon-Woo;Jeong, Woo-Jin;Ahn, Jae-Geun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.3
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    • pp.527-535
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    • 2021
  • Traditional business intelligence (BI) systems have been used widely as tools for better decision-making on time. On the other hand, building a data warehouse (DW) for the efficient analysis of rapidly growing data is time-consuming and complex. In particular, the ETL (Extract, Transform, and Load) process required to build a data warehouse has become much more complex as the BI platform moves to a cloud environment. Various BI solutions based on the NoSQL database, such as MongoDB, have been proposed to overcome these ETL issues. Decision-makers want easy access to data without the help of IT departments or BI experts. Recently, self-service BI (SSBI) has emerged as a way to solve these BI issues. This paper proposes a self-service BI system with farming data using the MongoDB cloud as DW to support the selection of new crops by return-farmers. The proposed system includes functions to provide insights to decision-makers, including data visualization using MongoDB charts, reporting for advanced data search, and monitoring for real-time data analysis. Decision makers can access data directly in various ways and can analyze data in a self-service method using the functions of the proposed system.