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In order to build relevant, useful, and effective recommender systems, researchers need to understand why users come to these systems and how users judge recommendation lists. Today, researchers use accuracy-based metrics for judging goodness. Yet these metrics cannot capture users' criteria for judging recommendation usefulness. We need to rethink recommenders from a user's perspective: they help users find new information. Thus, not only do we need to know about the user, we need to know what the user is looking for. In this dissertation, we explore how to tailor recommendation lists not just to a user, but to the user's current information seeking task. We argue that each recommender algorithm has specific strengths and weaknesses, different from other algorithms. Thus, different recommender algorithms are better suited for specific users and their information seeking tasks. A recommender system …