By Topic

Effects of Session Representation Models on the Performance of Web Recommender Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Demir, G.N. ; Istanbul Tech. Univ., Istanbul ; Goksedef, M. ; Etaner-Uyar, A.S.

Usage pattern discovery is a crucial part of Web recommendation systems and is strongly related with the performance of the recommender. User sessions which are constructed from Web access logs give information about a user's navigational behavior. These session records are one of the main resources in the pattern discovery stage. The representation of the sessions can be in different forms such as feature vectors based on page durations or existence/nonexistence of pages. This work analyzes the effects of the different recommendation models which consider different characteristics of user sessions. For this purpose, we used three different recommender models. The first one considers only the existence of the visited pages in a session and the view time of each page. The second recommender model considers only the order of the visited pages in each session. The third model is based on the co-occurrence of the visited pages among user sessions. Our experimental results show that using the ordering information improves the prediction accuracy of the next request.

Published in:

Data Engineering Workshop, 2007 IEEE 23rd International Conference on

Date of Conference:

17-20 April 2007