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User Profiles for Personalizing Digital Libraries

User Profiles for Personalizing Digital Libraries

Giovanni Semeraro, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops
Copyright: © 2009 |Pages: 10
ISBN13: 9781599048796|ISBN10: 1599048795|EISBN13: 9781599048802
DOI: 10.4018/978-1-59904-879-6.ch015
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MLA

Semeraro, Giovanni, et al. "User Profiles for Personalizing Digital Libraries." Handbook of Research on Digital Libraries: Design, Development, and Impact, edited by Yin-Leng Theng, et al., IGI Global, 2009, pp. 149-158. https://doi.org/10.4018/978-1-59904-879-6.ch015

APA

Semeraro, G., Basile, P., de Gemmis, M., & Lops, P. (2009). User Profiles for Personalizing Digital Libraries. In Y. Theng, S. Foo, D. Goh, & J. Na (Eds.), Handbook of Research on Digital Libraries: Design, Development, and Impact (pp. 149-158). IGI Global. https://doi.org/10.4018/978-1-59904-879-6.ch015

Chicago

Semeraro, Giovanni, et al. "User Profiles for Personalizing Digital Libraries." In Handbook of Research on Digital Libraries: Design, Development, and Impact, edited by Yin-Leng Theng, et al., 149-158. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-59904-879-6.ch015

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Abstract

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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