Abstract
Information systems that utilise contextual information have the potential of helping a user identify relevant information more quickly and more accurately than systems that work the same for all users and contexts. Contextual information comes in a variety of types, often derived from records of past interactions between a user and the information system. It can be individual or group based. We are focusing on the latter, harnessing the search behaviour of cohorts of users, turning it into a domain model that can then be used to assist other users of the same cohort. More specifically, we aim to explore how such a domain model is best utilised for profile-biased summarisation of documents in a navigation scenario in which such summaries can be displayed as hover text as a user moves the mouse over a link. The main motivation is to help a user find relevant documents more quickly. Given the fact that the Web in general has been studied extensively already, we focus our attention on Web sites and similar document collections. Such collections can be notoriously difficult to search or explore. The process of acquiring the domain model is not a research interest here; we simply adopt a biologically inspired method that resembles the idea of ant colony optimisation. This has been shown to work well in a variety of application areas. The model can be built in a continuous learning cycle that exploits search patterns as recorded in typical query log files. Our research explores different summarisation techniques, some of which use the domain model and some that do not. We perform task-based evaluations of these different techniques-thus of the impact of the domain model and profile-biased summarisation-in the context of Web site navigation.