• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.607-610
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• 2005

인터넷을 이용한 정보의 공유, 활용 및 전자상거래가 활성화되면서 많은 양의 컨텐츠가 웹 사이트에서 서비스되고 있다. 사용자는 신속하게 정보를 획득하기 위하여 웹 브라우저의 즐겨찾기 기능을 이용한다. 기존의 즐겨찾기 기능은 사용자가 해당 URL을 즐겨찾기에 등록할 것인지를 판단하고 수작업으로 등록하여 관리해야하는 문제점을 가지고 있다. 본 논문에서는 즐겨찾기 목록 관리의 문제점을 해결하기 위하여 사용자가 웹 브라우저를 이용하여 사이트 방문시 발생하는 클릭 스트림을 사용자 컴퓨터에 저장하고 그 자료를 분석하여 즐겨찾기 목록에 해당 URL을 자동으로 등록하고, 또한 즐겨찾기 목록이 동적으로 관리될 수 있는 즐겨찾기 메뉴 자동생성 시스템을 구현 하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.717-720
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• 2004

인터넷 사용 환경이 편리해지고 일상생활화되면서 웹 사이트 운영자들은 다양한 컨텐츠 제공과 차별화된 웹 서비스의 제공을 위해 노력하고 있다. 본 논문에서는 웹 사이트에서 발생하는 사용자의 클릭스트림 정보를 사용자별로 구분하여 분석하고. 분석된 클릭스트림 정보를 이용해 사용자에게 가장 관심있는 컨텐츠를 효과적으로 전할할 수 있는 사용자별 메뉴를 생성하는 시스템을 구현하였다. 웹 사이트에서 사용자가 자주 이용하는 컨텐츠 메뉴를 별도로 구성하여 제공함으로써. 사용자가 필요로하는 컨텐츠를 접근하기 위해 소요되는 시간을 절약할 수 있을 뿐만 아니라, 연관성있는 컨텐츠 메뉴를 함께 제공함으로서 사용자 중심의 차별화된 서비스를 제공할 수 있다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.19-27
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• 2011

Recommendation systems are developed to overcome the problems of selection and to promote intention to use. In this study, we propose a recommendation system using adjacency data according to user's behavior over time. For this, the product adjacencies are identified from the adjacency matrix based on graph theory. This research finds that there is a trend in the users' behavior over time though product adjacency fluctuates over time. The system is tested on its usability. The tests show that implementing this recommendation system increases users' intention to purchase and reduces the search time.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.82-86
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• 2012

In For the past few years, query log data has been collected to find user's behavior in using the site. Many researches have studied on the usage of query logs to extract user's preference, recommend personalization, improve caching and pre-fetching of Web objects, build better adaptive user interfaces, and also to improve Web search for a search engine application. A query log contain data such as the client's IP address, time and date of request, the resources or page requested, status of request HTTP method used and the type of browser and operating system. A query log can offer valuable insight into web site usage. A proper compilation and interpretation of query log can provide a baseline of statistics that indicate the usage levels of website and can be used as tool to assist decision making in management activities. In this paper we want to discuss on the tasks performed of query logs in pre-processing of web usage mining. We will use query logs from an online newspaper company. The query logs will undergo pre-processing stage, in which the clickstream data is cleaned and partitioned into a set of user interactions which will represent the activities of each user during their visits to the site. The query logs will undergo essential task in pre-processing which are data cleaning and user identification.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.311-328
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• 2022

Because of the spread of smartphones due to the development of information and communication technology, online shopping mall services can be used on computers and mobile devices. As a result, the number of users using the online shopping mall service increases rapidly, and the types of products traded are also growing. Therefore, to maximize profits, companies need to provide information that may interest users. To this end, the recommendation system presents necessary information or products to the user based on the user's past behavioral data or behavioral purchase records. Representative overseas companies that currently provide recommendation services include Netflix, Amazon, and YouTube. These companies support users' purchase decisions by recommending products to users using ratings, purchase records, and clickstream data that users give to the items. In addition, users refer to the ratings left by other users about the product before buying a product. Most users tend to provide ratings only to products they are satisfied with, and the higher the rating, the higher the purchase intention. And recently, e-commerce sites have provided users with the ability to vote on whether product reviews are helpful. Through this, the user makes a purchase decision by referring to reviews and ratings of products judged to be beneficial. Therefore, in this study, the correlation between the product rating and the helpful information of the review is identified. The valuable data of the evaluation is reflected in the recommendation system to check the recommendation performance. In addition, we want to compare the results of skipping all the ratings in the traditional collaborative filtering technique with the recommended performance results that reflect only the 4 and 5 ratings. For this purpose, electronic product data collected from Amazon was used in this study, and the experimental results confirmed a correlation between ratings and review usefulness information. In addition, as a result of comparing the recommendation performance by reflecting all the ratings and only the 4 and 5 points in the recommendation system, the recommendation performance of remembering only the 4 and 5 points in the recommendation system was higher. In addition, as a result of reflecting review usefulness information in the recommendation system, it was confirmed that the more valuable the review, the higher the recommendation performance. Therefore, these experimental results are expected to improve the performance of personalized recommendation services in the future and provide implications for e-commerce sites.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.