Development of a Web sites recommendation system

Pao Yue-kong Library Electronic Theses Database

Development of a Web sites recommendation system

 

Author: So, Hon-chu
Title: Development of a Web sites recommendation system
Year: 2001
Subject: Web sites
Internet searching
World Wide Web
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Computing
Pages: 80 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1599588
URI: http://theses.lib.polyu.edu.hk/handle/200/5197
Abstract: As the size of the WWW growth explosively and the semi-structured nature of the textual data in the Internet, it becomes more and more difficult to extract useful knowledge from it. Although existing search engines can offer a relief and extract information for the user, visiting the web search engine regularly to search for useful information is still a time consuming task. One way to help users in locating useful information is to directly provide such information for the users rather than make them search for it. System performing this task is regarded as one adopting a push-type model of information transmission. Common search engine system is based on the pull information transmission model. Links referring to favourite web sites of the user will be provided only after the user has submitted search request. One of the goals of this project is to design a mechanism for automated user profiling in which the user profile can be generated automatically and do not need to be created by users. The system will make use of the Web access log history for getting users favourites. The Web documents visited by the users will be clustered into groups and representatives of the groups represent the topics the users are interested in. So they can be used as the user profile. This project uses an interactive clustering algorithm to find groups in the Web documents. One of the advantages of using this algorithm is that the speed of the process is fast since it only scans the input documents once to perform clustering. Furthermore, the number of clusters defined is not needed to be provided in advance. This algorithm give good results and high quality document clusters.

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