An Intelligent Multi-Agent Recommender System for Human Capacity Building

TL;DR

This paper introduces an intelligent multi-agent recommender system that personalizes training course suggestions for engineering professionals, achieving high accuracy and adaptability through collaborative filtering and autonomous data retrieval.

Contribution

It presents a novel multi-agent framework for personalized course recommendation, integrating user modeling, data mining, and collaborative filtering for scalable and flexible suggestions.

Findings

90% ranking accuracy achieved

System is scalable and adaptable

Autonomous data retrieval enhances recommendations

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.