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Using Semantic Information for Web Usage Mining Based Recommendation
Date
2009-09-28
Author
Salın, Süleyman
Karagöz, Pınar
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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Web usage mining has become popular in various business areas related with Web site development. In Web usage mining, commonly visited navigational paths are extracted in terms of Web page addresses from the Web server visit logs, and the patterns are used in various applications including recommendation. The semantic information of the Web page contents is generally not included in Web usage mining. In this work, a framework for integrating semantic information with Web usage mining is presented. The frequent navigational patterns are extracted in the form of ontology instances instead of Web page addresses and the result is used for generating Web page recommendations to the visitor. In addition, an evaluation mechanism is implemented in order to test the success of the recommendation. Test results show that more accurate recommendations can be obtained by including semantic information in the Web usage mining.
Subject Keywords
Text categorization
,
Support vector machines
,
Linear discriminant analysis
,
Support vector machine classification
,
Frequency
,
Graphical models
,
Induction generators
,
Performance gain
,
Statistics
,
Classification algorithms
URI
https://hdl.handle.net/11511/48522
DOI
https://doi.org/10.1109/iscis.2009.5291819
Collections
Department of Computer Engineering, Conference / Seminar