Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
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
Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to recommend from positive evidence
Proceedings of the 5th international conference on Intelligent user interfaces
Information Retrieval
Bayesian online classifiers for text classification and filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
An innovative approach to intelligent information filtering
Proceedings of the 2006 ACM symposium on Applied computing
A new approach to intelligent text filtering based on novelty detection
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
A new approach to intelligent text filtering based on novelty detection
ADC '06 Proceedings of the 17th Australasian Database Conference - Volume 49
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The consideration of underlying analysis of user's information need is a key requirement in an intelligent filtering environment. However, the majority of current approaches to filtering are relevance-oriented, rather than user-oriented. This is partly because they are issued from fields that have somewhat different perspectives from that of information filtering, but also because of the difficulty of understanding and measuring user's motivations and the way in which the user expects the system to respond. This paper presents an original approach to information analysis and filtering inspired by the novelty detection theory. As well as being able to accurately learn user's information need, the approach has an analytical capacity for better understanding user's need. It provides a new way of looking at user's need in terms of precise, broad, and contradictory profile-contributing criteria. These criteria go on to estimate the relative importance the user might attach to precision and recall. The filtering threshold is then adjusted taking into account this knowledge about user's need. Experimental results on the standard Reuters-21578 collection prove the effectiveness of the approach and confirm the potential usefulness of adapting the filtering results according to the knowledge acquired about user's need.