Pictures of relevance: a geometric analysis of similarity measures
Journal of the American Society for Information Science
Using latent semantic indexing for information filtering
COCS '90 Proceedings of the ACM SIGOIS and IEEE CS TC-OA conference on Office information systems
Automatic thesaurus construction using Bayesian networks
Information Processing and Management: an International Journal - Special issue: history of information science
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
ICDT '01 Proceedings of the 8th International Conference on Database Theory
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using bayesian priors to combine classifiers for adaptive filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Utility-based information distillation over temporally sequenced documents
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Autopoiesis, the immune system, and adaptive information filtering
Natural Computing: an international journal
Proceedings of the twelfth international workshop on Web information and data management
Multi-step classification approaches to cumulative citation recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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The Vector Space Model has been and to a great extent still is the de facto choice for profile representation in content-based Information Filtering. However, user profiles represented as weighted keyword vectors have inherent dimensionality problems. As the number of profile keywords increases, the vector representation becomes ambiguous, due to the exponential increase in the volume of the vector space and in the number of possible keyword combinations. We argue that the complexity and dynamics of Information Filtering require user profile representations which are resilient and resistant to this "curse of dimensionality". A user profile has to be able to incorporate many features and to adapt to a variety of interest changes. We propose an alternative, network-based profile representation that meets these challenging requirements. Experiments show that the network profile representation can more effectively capture additional information about a user's interests and thus achieve significant performance improvements over a vector-based representation comprising the same weighted keywords.