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Handbook of Research on Digital Libraries: Design, Development, and Impact
A group of data elements that are considered semantically equivalent for the purposes of information retrieval.
Published in Chapter:
User Profiles for Personalizing Digital Libraries
Giovanni Semeraro (University of Bari, Italy), Pierpaolo Basile (University of Bari, Italy), Marco de Gemmis (University of Bari, Italy), and Pasquale Lops (University of Bari, Italy)
DOI: 10.4018/978-1-59904-879-6.ch015
Exploring digital collections to find information relevant to a user’s interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. The main focus of this chapter is the adoption of machine learning to build user profiles that capture user interests from documents. Profiles are used for intelligent document filtering in digital libraries. This work suggests the exploiting of knowledge stored in machine-readable dictionaries to obtain accurate user profiles that describe user interests by referring to concepts in those dictionaries. The main aim of the proposed approach is to show a real-world scenario in which the combination of machine learning techniques and linguistic knowledge is helpful to achieve intelligent document filtering.
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