Characterizing browsing strategies in the World-Wide Web
Proceedings of the Third International World-Wide Web conference on Technology, tools and applications
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Modeling Browsing Behavior at Multiple Websites
Marketing Science
Fast webpage classification using URL features
Proceedings of the 14th ACM international conference on Information and knowledge management
Personalized Presentation in Web-Based Information Systems
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Predicting structured objects with support vector machines
Communications of the ACM - Scratch Programming for All
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using domain ontology for semantic web usage mining and next page prediction
Proceedings of the 18th ACM conference on Information and knowledge management
Lifting Events in RDF from Interactions with Annotated Web Pages
ISWC '09 Proceedings of the 8th International Semantic Web Conference
AWIC'03 Proceedings of the 1st international Atlantic web intelligence conference on Advances in web intelligence
A framework for mining meaningful usage patterns within a semantically enhanced web portal
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Using Ontology and Sequence Information for Extracting Behavior Patterns from Web Navigation Logs
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
A Comprehensive Study of Features and Algorithms for URL-Based Topic Classification
ACM Transactions on the Web (TWEB)
Ontology-based filtering mechanisms for web usage patterns retrieval
EC-Web'05 Proceedings of the 6th international conference on E-Commerce and Web Technologies
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Large amounts of data are being produced daily as detailed records of Web usage behavior, but the task of deriving actionable knowledge from them remains a challenge. Investigations of user browsing behavior at multiple websites, while more beneficial than studies restricted to a single site, still need to tackle the problems of information heterogeneity and mapping usage logs to meaningful events from the application domain. Focusing on the problem of modeling cross-site browsing behavior, we present a formalization approach based on a Web browsing Activity Model (WAM). We introduce a novel two-staged approach for the semantic enrichment of usage logs with domain knowledge, bringing together Semantic Web technologies and Machine Learning techniques. For learning the semantic types of logs, we present a supervised multi-class classification formulation, deploying structural Support Vector Machines with new sequential input features. We provide an implementation of these approaches and show the results of evaluation with real-world data.