• The Journal of Information Technology
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• v.7 no.4
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• pp.9-20
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• 2004

Result Dos that materialization KNU Virus Wall to solve serious problem Hurtfulness Virus is present network chiefly in this research to do not become and do correct disposal in situation such as internet and Multiple Protocol that is done intelligence anger for ESM, CIS and MIS side as secondary to solve this problem about out log analysis system embody. As a result, could use comprehensively, and can click by Site Design, Packet transmission, and used to interior internet (GroupWare) in information protection aspect because intelligence enemy to face each other ESM's various hacking and virus uses Enterprise Security Management system and CIS, whole web through Smart View and relation of security could do monitoring.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.1-11
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• 2018

Recently, the volume of online shopping market continues to be fast-growing, that is important to provide customized service based on customer behavior evaluation analysis. The existing systems only provide analysis data on the profiles and behaviors of the consumers, and there is a limit to the processing in real time due to disk based mining. There are problems of accuracy and system performance problems to apply existing systems to web services that require real-time processing and analysis. Therefore, The system proposed in this paper analyzes the web click log streams generated in real time to calculate the concentration level of specific products and finds interested customers which are likely to purchase the products, and provides and intensive promotions to interested customers. And we verify the efficiency and accuracy of the proposed system.

• Proceedings of the KAIS Fall Conference
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• 2009.12a
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• pp.793-797
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• 2009

최근 정보검색분야에서는 사용자의 검색 의도를 이해하거나 효과적으로 결과를 전달하고자 하는 시도가 많이 이루어지고 있다. 그러나 현재 제공되고 있는 시스템은 현재 검색 사용자의 의도가 아닌 타인의 의도가 반영된 결과로 실제 사용자의 의도와 상이할 수 있으며, 사용자가 의도하는 바를 유효하게 반영하는 검색 결과를 제시하는 데는 아직 미흡한 실정이다. 따라서 사용자가 원하는 정보를 쉽게 발견할 수 있도록 검색어와 관련된 의도 정보를 제공하거나 검색 결과를 효율적으로 클러스터링 하여 전달하는 기능이 검색의 유용성을 증대시킬 수 있다. 본 논문에서는 검색어에서 사용자의 검색 의도를 자동으로 파악하여 그 의도에 맞는 검색 결과를 제공하기 위하여 사용자 클릭 로그를 사용하여 의도에 맞는 검색결과를 제공하는 방법에 대하여 제안한다.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.267-273
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• 2020

검색 엔진에 입력되는 질의 중 입력 빈도는 낮지만 상대적으로 길이가 긴 질의를 롱테일 질의라고 일컫는다. 롱테일 질의가 전체 검색 로그에서 차지하는 비중은 높은 반면, 그 형태가 매우 다양하고 검색 의도가 상세하며 개별 질의의 양은 충분하지 않은 경우가 많기 때문에 해당 질의에 대한 적절한 검색어를 추천하는 것은 어려운 문제다. 본 논문에서는 롱테일 질의 입력 시 적절한 검색어 추천을 제공하기 위하여 질의-문서 클릭 정보를 활용한 추출기반 모델 및 Seq2seq와 GPT-2 기반 생성모델을 활용한 질의 확장 방법론을 제안한다. 실험 및 결과 분석을 통하여 제안 방법이 기존에 대응하지 못했던 롱테일 질의를 자연스럽게 확장할 수 있음을 보였다. 본 연구 결과를 실제 서비스에 접목함으로써 사용자의 검색 편리성을 증대하는 동시에, 언어 모델링 기반 질의 확장에 대한 가능성을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2059-2071
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• 2007

If the correct structure of Web site is known, the information provider can discover users# behavior patterns and characteristics for better services, and users can find useful information easily and exactly. There may be some difficulties, however, to extract the exact structure of Web site because documents one the Web tend to be changed frequently. This paper proposes new method for extracting such Web structure automatically. The method consists of two phases. The first phase extracts the hyperlinks among Web documents, and then constructs a directed graph to represent the structure of Web site. It has limitations, however, to discover the hyperlinks in Flash and Java Applet. The second phase is to find such hidden hyperlinks by using Web access log. It fist extracts the click streams from the access log, and then extract the hidden hyperlinks by comparing with the directed graph. Several experiments have been conducted to evaluate the proposed method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.231-241
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• 2019

Web Usage Mining (WUM) is one part of Web mining and also the application of data mining technique. Web mining technology is used to identify and analyze user's access patterns by using web server log data generated by web users when users access web site. So first of all, it is important that the data should be acquired in a reasonable way before applying data mining techniques to discover user access patterns from web log. The main task of data acquisition is to efficiently obtain users' detailed click behavior in the process of users' visiting Web site. This paper mainly focuses on data acquisition stage before the first stage of web usage mining data process with activities like data acquisition strategy and field extraction algorithm. Field extraction algorithm performs the process of separating fields from the single line of the log files, and they are also well used in practical application for a large amount of user data.

• Journal of the Korean Society for Library and Information Science
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• v.39 no.2
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• pp.147-160
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• 2005

This study examines trends of web query types and topics submitted to NAVER during one year period by analyzing query logs and click logs. There was a seasonal difference in the distribution of query types. Query type distribution was also different between weekdays and weekends, and between different days of the week. The log data show seasonal changes in terms of the topics of queries. Search topics seem to change between weekdays and weekends, and between different days of the week. However, there was little change in overall patterns of search behavior across one year. The implications for system designers and web content providers are discussed.

• Journal of the Korean Society for information Management
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• v.34 no.3
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• pp.89-107
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• 2017

This study aims to compare information seeking behavior of 1300K users on PC and mobile phone. Transaction logs of 1300K, a major Korean shopping search engine, were analyzed. These transaction logs were collected over 1 month period, from April 1 to April 31, 2016. The results of this study show that there are a little more mobile sessions than PC sessions. Users conducted slightly more browsing on PCs than mobile phones, whereas users submitted queries more than two times on mobile phones than on PCs. Users clicked more search results on PCs than mobile phones, whereas users made important decision makings such as product purchases more on mobile phones than PCs. Top queries and categories were similar between PCs and mobile phones. Queries and categories on mobile phones were more focused than queries on PCs. Overall, mobile search behavior is more simple, passive, and focused than PC search behavior. The results of this study can be implemented to the effective improvement and development of search services for different devices.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.706-712
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• 2015

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.1-19
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• 2023

Despite the growing importance of video-based social media content, such as vlogs, as a marketing tool in the travel industry, there is limited research on the characteristics that enhance engagement among potential travelers. This study explores the influence of emotional valence in YouTube travel content on viewer engagement, specifically likes and comments. We analyzed 4,619 travel-related YouTube videos from eight popular tourist cities. Using negative binomial regression analysis, we found that both positive and negative emotions significantly influence the number of likes received. Videos with higher positive emotions as well as negative emotions receive more likes. However, when it comes to the number of comments, only negative emotions showed a significant positive influence, while positive emotions had no significant impact. These findings offer valuable insights for marketers seeking to optimize engagement strategies on YouTube, considering the unique nature of travel products. Further research into the effects of specific emotions on engagement is warranted to improve marketing strategies. This study highlights the powerful impact of emotions on viewer engagement in the context of social media, particularly on YouTube.