We present a general framework that dynamically adapts the query-result ranking to the different information needs in order to improve the search experience for the individual user. We distinguish three different search goals, namely whether the user re-searches known information, delves deeper into a topic she is generally interested in, or satisfies an ad-hoc information need. We take an implicit relevance feedback approach that makes use of the user’s web interactions, however, vary what constitutes the examples of relevant and irrelevant information according to the user’s search mode. We show that incorporating user behavior data can significantly improve the ordering of top results in a real web search setting.