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Intelligent Information Personalization Leveraging Constraint Satisfaction and Association Rule Methods

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Advances in Artificial Intelligence (Canadian AI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4013))

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Abstract

Recommender systems, using information personalization methods, provide information that is relevant to a user-model. Current information personalization methods do not take into account whether multiple documents when recommended together present a factually consistent outlook. In the realm of content-based filtering, in this paper, we investigate establishing the factual consistency between the set of documents deemed relevant to a user. We approach information personalization as a constraint satisfaction problem, where we attempt to satisfy two constraints—i.e. user-model constraints to determine the relevance of a document to a user and consistency constraints to establish factual consistency of the overall personalized information. Our information personalization framework involves: (a) an automatic constraint acquisition method, based on association rule mining, to derive consistency constraints from a corpus of documents; and (b) a hybrid of constraint satisfaction and optimization methods to derive an optimal solution comprising both relevant and factually consistent documents. We apply our information personalization framework to filter news items using the Reuters-21578 dataset.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, B3H 1W5, Canada

    Syed Sibte Raza Abidi & Yan Zeng

Authors
  1. Syed Sibte Raza Abidi
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  2. Yan Zeng
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Editor information

Editors and Affiliations

  1. Departement of Computer Science and Software Engineering, Laval University, G1K 7P4, Québec, Canada

    Luc Lamontagne

  2. Département IFT-GLO, Pavillon Adrien-Pouliot, Université Laval, G1K-7P4, Québec, Canada

    Mario Marchand

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© 2006 Springer-Verlag Berlin Heidelberg

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Abidi, S.S.R., Zeng, Y. (2006). Intelligent Information Personalization Leveraging Constraint Satisfaction and Association Rule Methods. In: Lamontagne, L., Marchand, M. (eds) Advances in Artificial Intelligence. Canadian AI 2006. Lecture Notes in Computer Science(), vol 4013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11766247_12

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  • DOI: https://doi.org/10.1007/11766247_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34628-9

  • Online ISBN: 978-3-540-34630-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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