Abstract
Adaptive hypermedia systems offer the functionality to personalize the information experience as per a user-model. In this paper we present a novel content adaptation approach that views information personalization as a constraint satisfaction problem. Information personalization is achieved by satisfying two constraints: (1) relevancy constraints to determine the relevance of a document to a user and (2) co-existence constraints to suggest complementing documents that either provide reinforcing viewpoints or contrasting viewpoints, as per the user’s request. Our information personalization framework involves: (a) an automatic constraint acquisition method, based on association rule mining on a corpus of documents; and (b) a hybrid of constraint satisfaction and optimization methods to derive an optimal solution—i.e. personalized information. We apply this framework to filter news items using the Reuters-21578 dataset.
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© 2006 Springer-Verlag Berlin Heidelberg
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Abidi, S.S.R., Zeng, Y. (2006). An Adaptive Hypermedia System Using a Constraint Satisfaction Approach for Information Personalization. In: Wade, V.P., Ashman, H., Smyth, B. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2006. Lecture Notes in Computer Science, vol 4018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11768012_55
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DOI: https://doi.org/10.1007/11768012_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34696-8
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