Title :
Applying evolutionary algorithms to the problem of information filtering
Author :
Tjoa, A. Min ; Höfferer, Max ; Ehrentraut, Gunter ; Untersmeyer, Peter
Author_Institution :
Inst. of Software Technol., Vienna Univ. of Technol., Austria
Abstract :
This paper presents an intelligent information filtering system that learns from user feedback and behavior through evolutionary algorithms. By applying the learning abilities of a classifier system and genetic algorithms to the system, the following tasks can be performed: (1) reducing a user´s information overload; (2) predicting the actions that the users are supposed to do; and (3) prioritizing electronic mail.
Keywords :
cognitive systems; data reduction; electronic mail; feedback; genetic algorithms; learning (artificial intelligence); online front-ends; pattern classification; user modelling; CIFS; Cognitive Information Filtering System; classifier system; electronic mail prioritization; evolutionary algorithms; genetic algorithms; intelligent information filtering system; learning; user action prediction; user behavior; user feedback; user information overload reduction; Computer science; Evolutionary computation; Expert systems; Feedback; Genetic algorithms; Indexing; Information filtering; Information filters; Information retrieval; Intelligent systems;
Conference_Titel :
Database and Expert Systems Applications, 1997. Proceedings., Eighth International Workshop on
Conference_Location :
Toulouse, France
Print_ISBN :
0-8186-8147-0
DOI :
10.1109/DEXA.1997.617331
