• Journal of the Korean Society for Library and Information Science
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• v.39 no.1
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• pp.265-278
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• 2005

This study examines web query types and topics submitted to Naver during one year period by analyzing query logs and click logs. Query logs capture queries users submitted to the system, and click logs consist of documents users clicked and viewed. This study presents a methodology to classify query types and topics. A method for click log analysis is also suggested. When classified by query types, there are more site search queries than content search queries. Queries about computer/internet. entertainment, shopping. game, education rank hightest. The implications for system designers and web content providers are discussed.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.1-16
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• 2006

This paper empirically analyzed consumer price search behavior using Web log data of a Korean web site for price comparison. Consumer click-stream data of the site was used to test the effects of price level, product category, third party certification, reputation of retailers on click behavior. According to the descriptive statistics, 67.4% of shopbot users clicked the offer which was the lowest price returned in a search. We found that third party certification and reputation of retailers were significant determinants of clicking the lowest priced offer from legit analysis. We also applied Tobit regression analysis to estimate the price premium of the two determinants, but only reputation of retailers was found to have price premium of 4.9%.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.831-840
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• 2001

In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.

• 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.

• Journal of Korean Library and Information Science Society
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• v.42 no.1
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• pp.325-338
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• 2011

This study aims to investigate characteristics of most clicked documents of Naver's universal search service. In particular, this study analyzed characteristics of most clicked documents such as click ratio, collection distribution, and yearly distribution. Also, clicked documents were evaluated in terms of relevance, credibility, and currency. In conducting this study, query logs and click logs of unified search service were analyzed. The results of this study show that most clicks occurred in blog collection and average click concentration rate reached almost 50%. Also, the relevance and currency of most clicked documents were quite high, but credibility of these documents were on average level. The results of this study can be implemented to the portal's effective development of searching algorithm and interface.

• Journal of Korean Library and Information Science Society
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• v.39 no.1
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• pp.265-278
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• 2008

This study aims to perform an evaluation of unified search services provided by major Korean search portals, Naver, Daum, Yahoo-Korea, and Empas. These unified search services are evaluated in terms of the relevance of search results. In conducting this study, real queries that real users submitted were used. This study also utilized click logs that consist of documents users clicked and viewed. The results of this study can be implemented to the development and improvement of portal's unified search services. Users can refer to the results of this study in choosing unified search services from search portals.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1561-1566
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• 2017

Due to the advances in machine learning and artificial intelligence technologies, many new services have become available. Among such services, recommendation systems have already been successfully applied to commercial services and made profits as in online shopping malls. Most recommendation algorithms in commercial services are based on content analysis or explicit feedback rates as in movie recommendations. However, many online shopping malls have difficulties in content analysis or are lacking explicit feedbacks on their items, which results in no recommendation system for their items. Even for such service systems, user log data is easily available, and if recommendations are possible with such log data, the quality of their service can be improved. In this paper, we extract implicit feedback like click information for items from log data and provide a recommendation system based on the implicit feedback. The proposed system is applied to a real in-service online shopping mall.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.1
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• pp.377-393
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• 2011

This study aims to investigate trends of internet searching behavior of users of NAVER, a major Korean search portal. In particular, this study analyzed trends of query submission behaviors, behaviors related to typos, multimedia searching behaviors, and click behaviors. In conducting this study, query logs and click logs of unified search service were analyzed. The results of this study show that there were little changes in the topic and length of queries, the pattern of typos, and multimedia seeking behavior over a year's period. However, click counts of documents have gradually increased over time. The results of this study can be implemented to increase the portal's effective development of internet contents and searching algorithms.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.19-37
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• 2014

Server transaction logs containing complete click-through data from a digital library of primarily image-based documents were analyzed to better understand user search session behavior. One month of data was analyzed using descriptive statistics and network analysis methods. The findings reveal iterative search behaviors centered on result views and evaluation and topical areas of focus for the search sessions. The study is novel in its combined analytical techniques and use of click-through data for image collections.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.