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Digital libraries are providing an increasing amount of data, which is normally structured in a classical way by documents and described by metadata as keywords. The data, even in scientific systems such as digital libraries and virtual research environments, will contain a great amount of noise or information unnecessary for our personal interests. Although there has been a lot of progress in the field of information retrieval, search techniques and other content finding methods, there is still much to be done in the field of information retrieval based on user behavior. This paper presents an approach deployed in the Humboldt Digital Library (HDL) to facilitate the retrieval of relevant information to the users of the system, making recommendations of paragraphs based on their profile and the behavior of other users who share similar profiles. The Humboldt digital library represents an innovative system of open access to the legacy of Alexander von Humboldt in a digital form on the Internet (www.avhumboldt.net). It contributes to the key question, how to present interconnected data in a proper form using information technologies.
After approximately 200 years, a comprehensive access to the texts of Humboldt’s extraordinary exploration of the Americans is within sight. To open the legacy to the public for free access the Humboldt Digital Library (HDL) project has been developing a dynamic amount of data related to studies of Alexander von Humboldt. The library includes a range of texts, tables and images, as well as many tools that assist mining the data and navigating the system.