An Experience Report on using the EDON Method for Building a Team Recommender System

Abstract

Team Recommender Systems (TRS) have become extremely common in recent years because they are software tools and techniques that helps to organizations to composite team needed to carry out a task requiring multiple skills. TRS have two important problems: (1) managing semantic heterogeneity that occurs when the data describing the same entities related to the real world is represented in different ways, and (2) specialization excess leading to display the objects of highest similarity with the user specified instead of a wide range of options leaving out of consideration the highest possible user interest information. On the other hand, recently, the ontology-based information systems have gained the attention of the researchers and practitioners since they handle the semantic heterogeneity problem. In this paper we report our experience in using the EDON methodology to build a TRS that analyses human resource information to recommend a work team for a software development project.