This thesis focuses on the field of Job Recommendation. Particularly, we focus on using implicit preferences exhibited by the job seeker in interactions with a web platform to propose an improved ranking algorithm for a job recommendation platform called Magnet.me. We also study evaluation of relevance, and evaluation of recommendation sorting algorithms to determine the degree of improvement achieved by the proposed algorithm. Using NDCG with different relevance evaluations, we test performance of the proposed algorithm in an online experiment on the job recommendation platform. We find that the evaluation of relevance strongly affects the distinguishability of NDCG. The evaluation shows that our sorting algorthm outperforms the original algorithm when using classical binary relevance, or relevance evaluations that consider items with negative feedback less relevant than items with missing feedback. However, when using relevance evaluations for NDCG that punish missing feedback more than negative feedback, NDCG loses its capability of distinguishing between algorithm performance. Based on baseline sorting algorithm evaluation MRR and the different evaluations using NDCG, we conclude that the proposed recommendation sorting algorithm outperforms the original algorithm.