Incremental relevance feedback for information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning while filtering documents
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
The score-distributional threshold optimization for adaptive binary classification tasks
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Maximum likelihood estimation for filtering thresholds
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
The dark side of information: overload, anxiety and other paradoxes and pathologies
Journal of Information Science
What Happened to Content-Based Information Filtering?
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
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Implementing, configuring, and running an information filtering system in a practical setting is a difficult and challenging problem. This is due to variety and configuration of available system components along with additional factors such as topic length, feedback, and system training. Moreover, the interplay between the different components and additional factors can lead to degraded system performance when adding or manipulating particular components. We explore the interactions and effects of different components and some of the factors with respect to performance. The main contribution of this paper is a better understanding of how to configure filtering systems along with the possible pitfalls of applying conflicting components which harm performance and result in a poor user experience.