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 in 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. R ECOMMENDER systems, for advising or recommending subject matter, are emerging as a growing application and research field [1] . These systems help users deal with information overload [1] . Information overload results from the availability of a large number of information on any topic in this age. Recommender systems form decision support systems for individuals as well as whole organisations by customising and recommending information. Recommender systems use computational intelligence and can be trained to find patterns between different users and subject matter. In this paper the problem of recommending training courses for engineering professionals is tackled. This paper presents a system that recommends training courses users by reducing the information overload by using information customisation as well as recommendation. The system further reduces the need for users to search a wide variety of sources of information as well as any need to monitor these sources for changes and updates. The scope of recommendation has been reduced to Electrical and Mechanical engineering professionals. Firstly the
Marivate et al. REF took MAS approach to solve the problem of recommending training courses to engineering professionals; the system reduces the need for users to search a wide variety of sources of information as well as dispensing with any need to monitor these sources for changes and updates.
An Intelligent Multi-Agent Recommender System for Human Capacity Building
MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference
MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference
Computer Science