Advertisement
Published in:
2013 | OriginalPaper | Chapter
Exploiting Click Logs for Adaptive Intranet Navigation
Authors : Sharhida Zawani Saad, Udo Kruschwitz
Published in: Advances in Information Retrieval
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.