As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents' rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the certainty-based preference completion. Hence, in this paper firstly, for alternative pairs with the obtained ranking set, a bijection has been built from the ranking space to the preference space, and the certainty and conflict of alternative pairs have been evaluated with a well-built statistical measurement Probability-Certainty Density Function on subjective probability, respectively. Then, a certainty-based voting algorithm based on certainty and conflict has been taken to conduct the certainty-based preference completion. Moreover, the properties of the proposed certainty and conflict have been studied empirically, and the proposed approach on certainty-based preference completion for partial rankings has been experimentally validated compared to state-of-arts approaches with several datasets.