Intelligent information-sharing systems
Communications of the ACM
Using latent semantic indexing for information filtering
COCS '90 Proceedings of the ACM SIGOIS and IEEE CS TC-OA conference on Office information systems
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Data mining: concepts and techniques
Data mining: concepts and techniques
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Modern Information Retrieval
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
A framework for learning constraints: Preliminary report
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
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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.