Using Semantic Information for Web Usage Mining Based Recommendation

Date

2009-09-28

Author

Salın, Süleyman
Karagöz, Pınar

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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

Collections

Department of Computer Engineering, Conference / Seminar