Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

From Building Information Modeling to City Information Modeling

With the development of Geographic Information System (GIS), the concept of digital city is implemented widely. However, in practice, most of the GIS models are relatively poorly attributed, semantically. Building Information Modeling (BIM) is a process involving the generation and management of digital representations of physical and functional characteristics of building, which is most used in small scale projects. In order to address the target problem of completing the semantic attribution of 3D digital city model, a framework of integrating BIM technology into GIS is demonstrated. A new concept of city information modeling (CIM) is proposed with the goal of bringing great benefits to the urban construction and city management. The composition of city information model is discussed. The data schema behind BIM and GIS (i.e. IFC and CityGML) are compared and mapped with each other. A case study of land planning of campus is demonstrated to present the potential benefits of CIM