Abstract
The IFT (Information Filtering Tool) project has the goal of developing new approaches to information filtering which are based on user modeling techniques for building and managing the representation of the user information preferences. In this paper we describe three prototypes which have been developed and evaluated within the project. All of them are dealing with textual semistructured documents and exploit a semantic network representation of user preferences: the first two prototypes (IFTool and PIFT) are characterized by two different matching algorithms utilized for assessing the relevance of an incoming document against the user model, whereas the third (ifWeb) concerns an application of IFTool to the navigation and filtering of documents in the INTERNET. The three prototypes have been evaluated in order to compare their performance with similar systems presented in the literature. The results achieved show that information filtering can positively profit from user modeling techniques, and point out interesting challenges for future investigations.
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Asnicar, F.A., Di Fant, M., Tasso, C. (1997). User model-based information filtering. In: Lenzerini, M. (eds) AI*IA 97: Advances in Artificial Intelligence. AI*IA 1997. Lecture Notes in Computer Science, vol 1321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63576-9_112
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DOI: https://doi.org/10.1007/3-540-63576-9_112
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