Knowledge-based artificial neural networks
Artificial Intelligence
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Machine Learning
Autonomous Agents and Multi-Agent Systems
A formal study of information retrieval heuristics
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Self Improving Coordination in Multi Agent Filtering Framework
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Agent technology for personalized information filtering: the PIA-system
Proceedings of the 2005 ACM symposium on Applied computing
The TREC robust retrieval track
ACM SIGIR Forum
Web Intelligence and Agent Systems
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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In a highly dynamic information society, the practical applicability of one filtering framework is usually directly proportional to its flexibility, where this assumes not only an easy integration of novel strategies but also the ability to adapt to new resource conditions. A major drawback of many existing systems, trying to make different synergies between filtering strategies, is usually concerned with an inability to easily integrate new strategies and with not taking care of resource availability, being critical for the realisation of the successful commercial deployments. The cornerstone of the presented filtering framework is in the encapsulation of the searching algorithms inside separate filtering agents whose abilities to be utilised in different runtime situations are efficiently learnt by combining both analytical and inductive learning. The evaluation results demonstrate that analytical learning successfully exploits domain knowledge about filtering strategies while helping inductive learning do faster adaptation.