• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.398-400
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• 2003

추천 시스템은 사용자의 아이템에 대한 선호도를 예측함으로써. 사용자에게 적합한 아이템을 추천한다. 이러한 추천 시스템은 희소성과 확장성의 문제를 안고 있다. 희소성이란 사용자의 선호도 예측의 토대가 되는 정보의 부족으로 인하여 추천 아이템의 범위가 제한되는 것이고, 확장성이란 사용자나 아이템의 수가 증가함에 따라 추천 시간이 증가하는 것이다. 본 논문에서는 아이템의 카테고리 정보를 이용한 다중 레벨 연관규칙을 선호도 예측에 적용하여 희소성과 확장성의 문제를 완화하고자 하였다. 연관규칙을 이용하여 선호도 예측을 위한 모델을 구축하여 확장성을 해결하고, 다중 레벨 연관규칙을 이용하여 추천 아이템의 범위를 확장할 수 있었다. 단일 레벨만을 사용한 방법과 비교한 결과, 다중 레벨을 사용한 방법이 좋은 성능을 보임을 확인할 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.2
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• pp.379-386
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• 2014

As the use of mobile device is increasing rapidly, the number of users is also increasing. However, most of the app stores are using recommendation of simple ranking method, so the accuracy of recommendation is lower. To recommend an item that is more appropriate to the user, this paper proposes a technique that reflects the weight of user information and recent preference degree of item. The proposed technique classifies the data set by categories and then derives a predicted value by applying the user's information weight to the collaborative filtering technique. To reflect the recent preference degree of item by categories, the average of items' rating values in the designated period is computed. An item is recommended by combining the two result values. The experiment result indicated that the proposed method has been more enhanced the accuracy, appropriacy, compared to item-based, user-based method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.198-199
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• 2023

자율운항선박 (Maritime Autonomous Surface Ship, MASS) 관련 기술이 전 세계적으로 활발히 개발되고 있는 가운데, 이러한 신기술을 검증하기 위해 국제표준 개발의 필요성이 증대하고 있다. 현재 ISO TC8(선박 및 해양기술) 분과에서 개발중이거나 제정된 표준 분석을 진행하였다. 3가지 카테고리로 분류하였을 때, 카테고리 3 - 세부 장비/어플리케이션에 대해 개발된 표준의 수가 카테고리 1 - 일반적인 가이드라인 및 기능적 요구사항에 대한 표준과 카테고리 1의 기능적 요구 사항을 만족하는 기술적 솔루션 레퍼런스인 기술적 솔루션 표준은 부족한 실정이다. 우선순위 도출을 통하여 카테고리 2 분야의 표준화 아이템을 도출하였고, 이를 기반으로 수립한 국제표준화 추진전략 및 계획을 소개할 예정이다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.543-551
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• 2015

As the use of internet and mobile devices became generalized, users utilizing search and recommendation in order to find the information they want in the midst of various websites have become common. In order to recommend more appropriate item for users, this paper proposes a recommendation technique that reflects the users' preference change following the flow of time by applying users' activity and time information. The proposed technique, after classifying the data in categories including the tag information that is considered at the time of choosing the items, only uses the data that users' preference change following the flow of time is reflected. For the users who prefer the corresponding category, the item that is extracted by applying tag information to collaboration filtering technique is recommended and for general users, items are recommended based on the ranking calculated by using the tag information. The proposed technique was experimented by using hetrec2011-movielens-2k data set. The experiment result indicated that the proposed technique has been more enhanced the accuracy, appropriacy, compared to item-based, user-based method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.341-344
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• 2004

웹 사용 마이닝(Web usage mining)은 웹 로그 파일(web log file)이나 웹 사용 데이터(Web usage data)에서 의미 있는 정보를 찾아내는 연구 분야이다. 웹 사용 마이닝에서 일반적으로 많이 사용하는 웹 로그 파일은 사용자들이 참조한 페이지의 단순한 리스트들이다. 따라서 단순히 웹 로그 파일만을 이용하는 방법만으로는 사용자가 참조했던 페이지의 내용을 반영하여 분석하는데에는 한계가 있다. 이러한 점을 개선하고자 본 논문에서는 페이지 위주가 아닌 웹 페이지가 포함하고 있는 내용(아이템)을 고려하는 새로운 퍼지 카테고리 기반의 웹 사용 마이닝 기법을 제시한다. 또한 사용자를 잘 파악하기 위해서 시간에 따라 관심의 변화를 파악하는 방법을 제시한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.88-90
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• 2021

This paper introduces a method for extracting subtitles from lecture videos through a Korean morpheme analyzer and classifying video categories according to the extracted morpheme information. In some cases incorrect information is entered due to human error and reflected in the characteristics of the items, affecting the accuracy of the recommendation system. To prevent this, we generate a keyword table for each category using morpheme information extracted from pre-classified videos, and compare the similarity of morpheme in each category keyword table to classify categories of Lecture videos using the most similar keyword table. These human intervention reduction systems directly classify videos and aim to increase the accuracy of the system.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.57-65
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• 2019

In the case of Domeggook B2B online shopping malls, it has a market share of over 70% with more than 2 million members and 800,000 items are sold per one day. However, since the same or similar items are stored and registered in different catalog entries, it is difficult for the buyer to search for items, and problems are also encountered in managing B2B large shopping malls. Therefore, in this study, we developed a catalog entry auto classification and recommendation system for products by using semi-supervised machine learning method based on previous huge shopping mall purchase information. Specifically, when the seller enters the item registration information in the form of natural language, KoNLPy morphological analysis process is performed, and the Naïve Bayes classification method is applied to implement a system that automatically recommends the most suitable catalog information for the article. As a result, it was possible to improve both the search speed and total sales of shopping mall by building accuracy in catalog entry efficiently.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.6
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• pp.1183-1190
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• 2017

With the development of network and IT technology, people are searching and purchasing items they want, not bounded by places. Therefore, there are various studies on how to solve the scalability problem due to the rapidly increasing data in the recommendation system. In this paper, we propose an item-based collaborative filtering method using Tag weight and a recommendation technique using MapReduce method, which is a distributed parallel processing method. In order to improve speed and efficiency, the proposed method classifies items into categories in the preprocessing and groups according to the number of nodes. In each distributed node, data is processed by going through Map-Reduce step 4 times. In order to recommend better items to users, item tag weight is used in the similarity calculation. The experiment result indicated that the proposed method has been more enhanced the appropriacy compared to item-based method, and run efficiently on the large amounts of data.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.289-334
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• 2018

Even though information in many languages other than English is quickly increasing, English is still playing the role of the lingua franca and being accounted for the largest proportion on the web. Therefore, it is necessary to investigate the key features and differences between "information searching behavior using mother tongue as a search language" and "information searching behavior using English as a search language" of users who are non-mother tongue speakers of English to acquire more diverse and abundant information. This study conducted the experiment on the web searching which is applied in concurrent think-aloud method to examine the information searching behavior and the cognitive process in Korean search and English search through the twenty-four undergraduate students at a private university in South Korea. Based on the qualitative data, this study applied the frequency analysis to web search pattern under search language. As a result, it is active, aggressive and independent information searching behavior in Korean search, while information searching behavior in English search is passive, submissive and dependent. In Korean search, the main features are the query formulation by extract and combine the terms from various sources such as users, tasks and system, the search range adjustment in diverse level, the smooth filtering of the item selection in search engine results pages, the exploration and comparison of many items and the browsing of the overall contents of web pages. Whereas, in English search, the main features are the query formulation by the terms principally extracted from task, the search range adjustment in limitative level, the item selection by rely on the relevance between the items such as categories or links, the repetitive exploring on same item, the browsing of partial contents of web pages and the frequent use of language support tools like dictionaries or translators.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.163-172
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• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.