Mobile Situation-Aware Task Recommendation Application

With more and more applications available on mobile devices, it has become increasingly difficult for users to find a desired application. Although research has been conducted for situation-awarere commendations on mobile devices, none addresses this problem; most research is for media content recommendations. Moreover, existing approaches assume predefined situations and/or user-specified profiles; some require users to intentionally train their devices before using them for recommendations. We believe that what defines a situation and what applications are preferred in the situation not only vary from user to user but also change over time, and therefore these assumptions and requirements are impractical for ordinary consumers. In this paper, we will describe our approach of using unsupervised learning, specifically co-clustering, to derive latent situation-based patterns from usage logs of user interactions with the device and environments and use the patterns for task and communication mode recommendations.