Over the last decade, the growth of e-recruitment has resulted in the expansion of web channels dedicated to candidate recruitment, making it easy to find and apply for jobs. However, as a result, today’s human resource managers are inundated with applications for each job opening. This leads to the production of significant number of documents, referred to as resumes or curriculum vitae (CV). Optimal processing of this data is necessary from a Human Resource strategic and economic perspective, where cost and time effectiveness is paramount. We propose an efficient ranking algorithm to overcome the high time and cost complexity associated with the pairwise comparison of candidates in the state-of-the-art Multi-Criteria Decision Making (MCDM) based ranking algorithm. This algorithm is integrated with matrix sorting and pruning based solution to enhance its scalability. Our proposed algorithm was tested on three different datasets: real-world recruitment, simulated DBLP, and synthetic datasets. Our algorithm shows promising results, which makes it effective and efficient on real-world resume ranking processes.