An intelligent Multi-Agent recommender system for human capacity building

  title={An intelligent Multi-Agent recommender system for human capacity building},
  author={Vukosi N. Marivate and George Ssali and Tshilidzi Marwala},
  journal={MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference},
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… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 14 extracted citations

Towards The Use of Advance Technology in E-Learning : A Review

Hesham Fakrany, Merza Abbas, Ebtisam Alqhtani
View 1 Excerpt


Publications referenced by this paper.
Showing 1-3 of 3 references

Accurately and reliably extracting data from the web : a machine learning approach

K. Lerman Knoblock, S. Minton, I. Muslea
Proceedings of the 5 th International symposium on Spatial Data Quality

Similar Papers

Loading similar papers…