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Nowadays, people have been increasingly interested in exploiting Web Search Engines (WSEs) not only for having access to simple Web pages, but mainly for carrying out even complex activities, namely Web-mediated processes (or taskflows). Therefore, users' information needs will become more complex, and (Web) search and recommender systems should change accordingly for dealing with this shift. We claim that such taskflows and their composing tasks are implicitly present in users' minds when they interact, thus, with a WSE to access the Web. Our first research challenge is thus to evaluate this belief by analyzing a very large, longterm log of queries submitted to a WSE, and associating meaningful semantic labels with the extracted tasks (i.e., clusters of task-related queries) and taskflows. This large knowledge base constitutes a good starting point for building a model of users' behaviors. The second research challenge is to devise a novel recommender system that goes beyond the simple query suggestion of modern WSEs. Our system has to exploit the knowledge base of Web-mediated processes and the learned model of users' behaviors, to generate complex insights and task-based suggestions to incoming users while they interact with a WSE.