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A Dynamic Recommendation System Using User Log Analysis and Document Similarity in Clusters  

김진수 (인하대학교 전자계산공학과)
김태용 (문경대학 인터넷정보계)
최준혁 (김포대학 컴퓨터계열 소프트웨어전)
임기욱 (선문대학교 산업공학)
이정현 (인하대학교 컴퓨터공학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.5, 2004 , pp. 586-594 More about this Journal
Abstract
Because web documents become creation and disappearance rapidly, users require the recommend system that offers users to browse the web document conveniently and correctly. One largely untapped source of knowledge about large data collections is contained in the cumulative experiences of individuals finding useful information in the collection. Recommendation systems attempt to extract such useful information by capturing and mining one or more measures of the usefulness of the data. The existing Information Filtering system has the shortcoming that it must have user's profile. And Collaborative Filtering system has the shortcoming that users have to rate each web document first and in high-quantity, low-quality environments, users may cover only a tiny percentage of documents available. And dynamic recommendation system using the user browsing pattern also provides users with unrelated web documents. This paper classifies these web documents using the similarity between the web documents under the web document type and extracts the user browsing sequential pattern DB using the users' session information based on the web server log file. When user approaches the web document, the proposed Dynamic recommendation system recommends Top N-associated web documents set that has high similarity between current web document and other web documents and recommends set that has sequential specificity using the extracted informations and users' session information.
Keywords
Recommendation System; Association Rule; Web Mining; Sequential Pattern; Clustering;
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