Colleague recommender system in the Expert Cloud using features matrix

Abstract

Purpose Expert Cloud as a new class of cloud systems enables its users to request and share the skill, knowledge and expertise of people by employing internet infrastructures and cloud concepts. Since offering the most appropriate expertise to the customer is one of the clear objectives in Expert Cloud, colleague recommendation is a necessary part of it. So, the purpose of this paper is to develop a colleague recommender system for the Expert Cloud using features matrices of colleagues. Design/methodology/approach The new method is described in two phases. In the first phase, all possible colleagues of the user are found through the filtering mechanism and next features of the user and possible colleagues are calculated and collected in matrices. Six potential features of colleagues including reputation, expertise, trust, agility, cost and field of study were proposed. In the second phase, the final score is calculated for every possible colleague and then top-k colleagues are extracted among users. The survey was conducted using a simulation in MATLAB Software. Data were collected from Expert Cloud website. The method was tested using evaluating metrics such as precision, accuracy, incorrect recommendation and runtime. Findings The results of this study indicate that considering more features of colleagues has a positive impact on increasing the precision and accuracy of recommending new colleagues. Also, the proposed method has a better result in reducing incorrect recommendation. Originality/value In this paper, the colleague recommendation issue in the Expert Cloud is pointed out and the solution approach is applied into the Expert Cloud website.