Recommendation agent using a routine model determined from mobile device data

A user's context history is analyzed to identify transitions between contexts therein. The identified transitions are used to build a routine model for the user. The routine model includes transition rules indicating a source context, a destination context, and, optionally, a probability that the user will transition from the source context to the destination context, based on the user's historical behavior. A customized recommendation agent for the user is built using the routine model. The customized recommendation agent selects recommendations from a corpus to present to the user, based on the routine model and the user's current or predicted future context.