Simple search engines have turned out to be successful in text retrieval. This is partly due to advances in algorithms, but additionally because the typical tasks are relatively straightforward as it is possible to come up with a good list of keywords with a reasonable effort. All information needs cannot however be solved with such searches. For the more and more commonly available audio-visual data people are not able to specify efficient non-textual search keys. Moreover, if the information needs of the user are tacit it is very laborious to create a query.We develop new information retrieval principles needed for replacing or complementing explicit queries. The new techniques will be applied to construct prototypes of proactive information navigators, the next generation systems able to adapt to the user and her tacit information needs. During browsing and searching with a task-dependent interface, the goals of the user will be inferred from implicit feedback signals and interaction, complemented with social filtering.New advanced machine learning methods are needed for inferring the implicit topic of interest and the sense in which it is interesting. We will augment the standard two-valued relevance feedback from the user by richer feedback derived from point-and-click and point-and-speak interfaces and gaze patterns over items that include texts, images and movies. Eye tracking technology is rapidly maturing to be usable on a massive scale.Our consortium combines pioneering application expertise with solid machine learning background. We have built a top content-based image retrieval system and used eye tracking in information retrieval. Our machine learning expertise is suited for the core issues of learning the comparison metrics and solving the exploration-exploitation problem. Proactive information navigators will bring up a new spectrum of application areas; the system will be tested in a set of targeted applications by two industry partners.