In many collaborative applications, a party who wishes to make a transaction requires that it has a certain level of trust in the other party. It is frequently the case that the parties are unknown to each other and thus share no pre-existing trust. Trust-based systems enable users to establish trust in unknown users through trust recommendation from known users. For example, Bob may choose to trust an unknown user Carol when he receives a recommendation from his friend Alice that Carol's trustworthiness is 0.8 on the interval [0,1].In this paper we highlight the problem that when a trust value is recommended by one user to another it may lose its real meaning due to subjectivity. Bob may regard 0.8 as a very high value of trust but it is possible that Alice perceived this same value as only average. We present a solution for the elimination of subjectivity from trust recommendation. We run experiments to compare our subjectivity-eliminated trust recommendation method with the unmodified method. In a random graph based web of trust with high subjectivity, it is observed that the novel method can give better results up to 95% of the time.