Predicting Smartphone Adoption in Social Networks.

The recent advancements in online social networks and mobile devices have provided valuable data sources to track users' smartphone adoption, i.e., the usage of smartphones over time. An incisive understanding of users' smartphone adoption can benefit many useful applications, ranging from user behavior understanding to targeted marketing. This paper studies smartphone adoption prediction in social networks by lever-aging the wisdom of an online world. A critical challenge along this line is to identify the key factors that underline people's adoption behaviors and distinguish the relative contribution of each factor. Specifically, we model the final smartphone status of each user as a result of three influencing factors: the social influence factor, the homophily factor, and the personal factor. We further develop a supervised model that takes all three factors for smartphone adoption and at the same time learns the relative contribution of each factor from the data. Experimental results on a large real world dataset demonstrate the effectiveness of our proposed model.