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| Content Provider | ACM Digital Library |
|---|---|
| Author | Agarwal, Sudhir Hoxha, Julia |
| Abstract | 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. |
| Starting Page | 488 |
| Ending Page | 495 |
| Page Count | 8 |
| File Format | |
| ISBN | 9780769548807 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-12-04 |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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