Improving the prediction of page access by using semantically enhanced clustering


Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Faculty of Engineering, Department of Computer Engineering, Turkey

Approval Date: 2014

Student: ERMAN ŞEN

Co-Supervisor: İSMAİL HAKKI TOROSLU, PINAR KARAGÖZ

Abstract:

There are many parameters that may affect the navigation behaviour of web users. Prediction of the potential next page that may be visited by the web user is important, since this information can be used for prefetching or personalization of the page for that user. One of the successful methods for the determination of the next web page is to construct behaviour models of the users by clustering. The success of clustering is highly correlated with similarity measure that is used for calculating the similarity among navigation sequences. This thesis proposes a new approach for determining the next web page by extending the standard clustering method with the content-based semantic similarity method. The success of the proposed method has also been shown through real life web log data.