An intelligent multi-agent recommender system for human capacity building

V. N. Marivate, G. Ssali, T. Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Citations (Scopus)

Abstract

This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative filtering recommendation is implemented using intelligent agents. The agents work together for recommending meaningful training courses and updating the course information. The system uses a users profile and keywords from courses to rank courses. A ranking accuracy for courses of 90% is achieved while flexibility is achieved using an agent that retrieves information autonomously using data mining techniques from websites. This manner of recommendation is scalable and adaptable. Further improvements can be made using clustering and recording user feedback.

Original languageEnglish
Title of host publicationMELECON 2008 - 2008 IEEE Mediterranean Electrotechnical Conference
Pages909-915
Number of pages7
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventMELECON 2008 - 2008 IEEE Mediterranean Electrotechnical Conference - Ajaccio, France
Duration: 5 May 20087 May 2008

Publication series

NameProceedings of the Mediterranean Electrotechnical Conference - MELECON

Conference

ConferenceMELECON 2008 - 2008 IEEE Mediterranean Electrotechnical Conference
Country/TerritoryFrance
CityAjaccio
Period5/05/087/05/08

Keywords

  • Data mining
  • Multiagent systems
  • Neural network
  • Recommendation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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