Due to size restrictions, mobile phone user interfaces are often difficult to use[8]. In this short paper, we investigated inducing shortcuts to replace the sequence of actions required to complete common tasks on a mobile phone. In particular, we used mobile phone interaction data to evaluate several methods for inducing shortcuts. We considered the balance between maximising interface efficiency and shortcuts that remained stable and hence predictable. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning—knowledge acquisition, parameter learning; H.5.2 [Information Interfaces and Presentation]: User Interfaces—evaluation/methodology General Terms Experimentation, Human Factors Keywords Adaptive User-Interface, Mobile Phone Interfaces, User Oriented Machine Learning