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Content Provider | IEEE Xplore Digital Library |
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Author | Ping Ni Jianxin Liao Chun Wang Keyan Ren |
Copyright Year | 2009 |
Abstract | In our study, we propose a web information recommendation framework depending on user behaviors. It is different from traditional recommendation systems depending on single association rule or classification engine. It has an interactive interface with people who could adjust his access manner according to the requirements by himself. It differs from the system which needs each reader to indicate whether or not he likes report in order to extract knowledge by offline analysis. In our system, people could select manners of recommendation, for example clustered information or associated information extracted from user activity history. Simultaneously, meaning clustered and associated information knowledge could be extracted accurately by recursion invoking between K-Means and Apriori. Therefore, surfers could initiatively discover various patterns meeting their requirements accurately. |
Starting Page | 426 |
Ending Page | 430 |
File Size | 321267 |
Page Count | 5 |
File Format | |
ISBN | 9780769535074 |
DOI | 10.1109/CSIE.2009.105 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2009-03-31 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Laboratories Telecommunication switching Apriori Association Rule Data mining Proposals Association rules Web Mining Computer science Electronics industry Web mining Internet Portals |
Content Type | Text |
Resource Type | Article |
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