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Journal of the American Society for Information Science
Detection of shifts in user interests for personalized information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
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Adaptive information agents in distributed textual environments
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A system for automatic personalized tracking of scientific literature on the Web
Proceedings of the fourth ACM conference on Digital libraries
Learning user interest dynamics with a three-descriptor representation
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Introduction to Modern Information Retrieval
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Information Retrieval
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User Modeling and User-Adapted Interaction
Information Filtering: Overview of Issues, Research and Systems
User Modeling and User-Adapted Interaction
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ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Immunity from spam: an analysis of an artificial immune system for junk email detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Assessing user-specific difficulty of documents
Information Processing and Management: an International Journal
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Content-based filtering can be deployed for personalised information dissemination on the web, but this is a possibility that has been largely ignored. Nowadays, there are no successful content-based filtering applications available online. Nootropia is an immune-inspired user profiling model for content-based filtering. It has the advantageous property to be able to represent a user's multiple interests and adapt to a variety of changes in them. In this paper we describe our early efforts to develop real world personalisation services based on Nootropia. We present, the architecture, implementation, usage and evaluation of the personalised news and paper aggregator, which aggregates news and papers that are relevant to an individual's interests. Our user study shows that Nootropia can effectively learn a user's interests and identify relevant information. It also indicates that information filtering is a complicated task with many factors affecting its successful application in a real situation.