With the proliferation of Web services, more and more functionally equivalent services are being published by service providers on the Web. Although more services mean more flexibility for consumers, it also increases the burden of choosing as consumers may have little or no past experience with the service they will interact with. Therefore, reputation systems have been proposed and are playing a crucial role in the service-oriented environment. Current reputation systems are mainly built upon the explicit feedback or rating given by consumers after experiencing the service. Unfortunately, services at the cold-start stage, prior to being rated, face the rating scarcity problem. In this paper, we focus on this problem and address it through a novel reputation model that uses the Elo algorithm to consider consumer-implicit information in a graph analysis approach. A theoretical analysis is conducted to identify the sufficient and necessary condition for the model to converge to a stable state. Furthermore, experiments confirm our model outperforms the widely adopted reputation algorithm in both accuracy and convergence in the situation of rating scarcity.