Data Mining for Measuring and Improving the Success of Web Sites
Data Mining and Knowledge Discovery
Clustering Web Surfers with Probabilistic Models in a Real Application
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Exploring mouse movements for inferring query intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
MouseHints: easing task switching in parallel browsing
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Interactive hypervideo visualization for browsing behavior analysis
Proceedings of the 21st international conference companion on World Wide Web
Web browsing behavior analysis and interactive hypervideo
ACM Transactions on the Web (TWEB)
| Hi-index | 0.00 |
In this paper we are interested in describing Web pages by how users interact within their contents. Thus, an alternate but complementary way of labelling and classifying Web documents is introduced. The proposed methodology is founded on unsupervised learning algorithms, aiming to automatically find natural clusters by means of users' implicit interaction data. Furthermore, it also copes with the dynamic nature and heterogeneity of both users' behaviour and the Web, updating the clustering model over time. We want to show that our framework can be easily integrated in any Website, just employing already-known methods and current technologies.