%0 Conference Paper %1 citeulike:5343440 %A Sahebi, Shaghayegh %A Oroumchian, Farhad %A Khosravi, Ramtin %B WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology %C Washington, DC, USA %D 2008 %I IEEE Computer Society %K clustering ontology personalization recommender %P 82--85 %R 10.1109/wiiat.2008.270 %T An Enhanced Similarity Measure for Utilizing Site Structure in Web Personalization Systems %U http://dx.doi.org/10.1109/wiiat.2008.270 %X The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based recommendation systems. In hybrid recommendation systems, other knowledge resources, like content, semantics, and hyperlink structure of the Web site, have been utilized to enhance usage-based personalization systems. In this study, we introduce a new structure-based similarity measure for user sessions. We also apply two clustering algorithms on this similarity measure to compare it to cosine and another structure-based similarity measures. Our experiments exhibit that adding structure information, leveraging the proposed similarity measure, enhances the quality of recommendations in both methods. %@ 978-0-7695-3496-1

@inproceedings{citeulike:5343440, abstract = {{The need for recommendation systems to ease user navigations has become evident by growth of information on the Web. There exist many approaches of learning for Web usage-based recommendation systems. In hybrid recommendation systems, other knowledge resources, like content, semantics, and hyperlink structure of the Web site, have been utilized to enhance usage-based personalization systems. In this study, we introduce a new structure-based similarity measure for user sessions. We also apply two clustering algorithms on this similarity measure to compare it to cosine and another structure-based similarity measures. Our experiments exhibit that adding structure information, leveraging the proposed similarity measure, enhances the quality of recommendations in both methods.}}, added-at = {2018-03-19T12:24:51.000+0100}, address = {Washington, DC, USA}, author = {Sahebi, Shaghayegh and Oroumchian, Farhad and Khosravi, Ramtin}, biburl = {https://www.bibsonomy.org/bibtex/26a79c407d2f1db88f8027f654c080e30/aho}, booktitle = {WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology}, citeulike-article-id = {5343440}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1487258}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/wiiat.2008.270}, doi = {10.1109/wiiat.2008.270}, interhash = {cf4e7749566ce38dd3375d4146cbb803}, intrahash = {6a79c407d2f1db88f8027f654c080e30}, isbn = {978-0-7695-3496-1}, keywords = {clustering ontology personalization recommender}, pages = {82--85}, posted-at = {2009-08-03 22:06:53}, priority = {0}, publisher = {IEEE Computer Society}, timestamp = {2018-03-19T12:24:51.000+0100}, title = {{An Enhanced Similarity Measure for Utilizing Site Structure in Web Personalization Systems}}, url = {http://dx.doi.org/10.1109/wiiat.2008.270}, year = 2008 }