With the rapid development of information technology, the virtual world platform has become a competitive market due to its prosperous future. Since the number of both users and virtual products has grown rapidly, recommender systems have come to play an important role in the virtual world in terms of solving information overload problems and obtaining benefits for both platform runners and users. In this paper, we argue that there are two important factors, social circle neighbors and the virtual house bandwagon effect, which affect users' preferences for virtual products. Hence, we propose two novel recommendation approaches that predict users' preferences based on an analysis of social influence between target user and his/her social circle neighbors, and the effect of the bandwagon phenomenon during the virtual house visit process respectively. The performance of the proposed approaches is evaluated by conducting experiments with a dataset collected from a virtual world platform in Taiwan. The experimental results show that the proposed approaches outperform the conventional recommendation methods, and also exhibit the effectiveness of turning to social influence and the bandwagon effect for improving recommendation accuracy in virtual worlds.