Authors:
Sahin Albayrak
and
Dragan Milosevic
Affiliation:
DAI-Lab, Technical University Berlin, Germany
Keyword(s):
Cooperation in multi agent systems, Distributed information retrieval, Intelligent information agents, Cooperative filtering communities, Self improving cooperation, Filtering framework, Recommendation systems
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Intelligent Agents
;
Internet Technology
;
Knowledge Management
;
Ontologies and the Semantic Web
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Web Information Systems and Technologies
Abstract:
In nowadays easy to produce and publish information society, filtering services have to be able to simultaneously search in many potentially relevant distributed sources, and to autonomously combine only the best found results. Ignoring a necessity to address information retrieval tasks in a distributed manner is a major drawback for many existed search engines which try to survive the ongoing information explosion. The essence of a proposed solution for performing distributed filtering is in both installing filtering communities around information sources and setting a comprehensive cooperation mechanism, which both takes care about how promising is each particular source and tries to improve itself during a runtime. The applicability of the presented cooperation among communities is illustrated in a system serving as intelligent personal information assistant (PIA). Experimental results show that integrated cooperation mechanisms successfully eliminate long lasting filtering jobs w
ith duration over 1000 seconds, and they do that within an acceptable decrease in feedback and precision values of only 3% and 6%, respectively.
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