With the increasing popularity of web service technology, many users turn to look for appropriate web services to further build their complex business applications. As an effective manner for service discovery, service recommendation technique is gaining ever-increasing attention, e.g., Collaborative Filtering (i.e., CF) recommendation. Generally, the traditional CF recommendation (e.g., user-based CF, item-based CF or hybrid CF) can achieve good recommendation results. However, due to the inherent sparsity of user-service rating data, it is possible that the target user has no similar friends and the services preferred by target user own no similar services. In this exceptional situation, traditional CF recommendation approaches cannot deliver an accurate recommendation result. In view of this shortcoming, a novel Social Balance Theory (i.e., SBT)-based service recommendation approach, i.e., RecSBT is introduced in this paper, to help improve the recommendation performance. Finally, through a set of simulation experiments deployed on MovieLens-1M dataset, we further validate the feasibility of RecSBT in terms of recommendation accuracy and recall.