Exploiting the hierarchical structure for link analysis
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A domain model of Web recommender systems based on usage mining and collaborative filtering
Requirements Engineering
Proceedings of the 16th international conference on World Wide Web
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Predicting user interests from contextual information
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Methods for Evaluating Interactive Information Retrieval Systems with Users
Foundations and Trends in Information Retrieval
Applying web usage mining for adaptive intranet navigation
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
Exploring ant colony optimisation for adaptive interactive search
ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory
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Web sites and intranets can be difficult to navigate as they tend to be rather static and a new user might have no idea what documents are most relevant to his or her need. Our aim is to capture the navigational behaviour of existing users (as recorded in the click logs) so that we can assist future users by proposing the most relevant pages as they navigate the site without changing the actual Web site and do this adaptively so that a continuous learning cycle is being employed. In this paper we explore three different algorithms that can be employed to learn such suggestions from navigation logs. We find that users managed to conduct the tasks significantly quicker than the (purely frequency-based) baseline by employing ant colony optimisation or random walk approaches to the log data for building a suggestion model.