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Web search engines answer user needs on a query-by-query fashion, namely they retrieve the set of the most relevant results to each issued query, independently. However, users often submit queries to perform multiple, related tasks. In this paper, we first discuss a methodology to discover from query logs the latent tasks performed by users. Furthermore, we introduce the Task Relation Graph (TRG) as a representation of users' search behaviors on a task-by-task perspective. The task-by-task behavior is captured by weighting the edges of TRG with a relatedness score computed between pairs of tasks, as mined from the query log. We validate our approach on a concrete application, namely a task recommender system, which suggests related tasks to users on the basis of the task predictions derived from the TRG. Finally, we show that the task recommendations generated by our solution are beyond the reach of existing query suggestion schemes, and that our method recommends tasks that user will likely perform in the near future.