Helping mobile apps bootstrap with fewer users

A growing number of mobile apps are exploiting smartphone sensors to infer user behavior, activity, or context. Inference requires training using labeled ground truth data. Obtaining labeled data for new apps is a "chicken-egg" problem. Without a reasonable amount of labeled data, apps cannot provide any service. But until an app provides useful service it is not worth installing and has no opportunity to collect user data. This paper aims to address this problem. Our intuition is that even though users are different, they exhibit similar patterns on certain sensing dimensions. For instance, different users may walk and drive at different speeds, but certain speeds will indicate driving for all users. These common patterns could be used as "seeds" to model new users through semi-supervised learning. We prototype a technique to automatically extract the commonalities to seed personalized inference models for new users. We evaluate the proposed technique through example apps and real world data.