Web 2.0 streams, like blog postings, microblogging tweets, or RSS feeds from online communities, offer a wealth of latest news about real-world events and societal discussion. From a user’s perspective, it becomes harder and harder to get a decent overview of recent events, given these massive streams of information that are continuously flowing. Ideally, a system would continuously put together recent information, ranked by the current social impact but also weighted by the users’ personal interests. In this work, we develop methods to meet these requirements. The presented approach continuously tracks the most popular tags attached to the incoming items and based on this, constructs a dynamic top-k query. By continuous evaluation of this query on the incoming stream, we are able to retrieve the currently hottest items. These hottest items are then fed into an engine that re-ranks them w.r.t. user specified interests, given in form of term based topic descriptions. This calls for high performance algorithms for efficient hot document retrieval and subsequently personalizing these documents based on user profiles, given the high rate of incoming data and the immense number of user profiles. In this work we present a combined solution, making use of our prior work on information filtering and showing how it can be used in combination with the current work, on how to continuously determine the hottest documents. To demonstrate the suitability of our approach, we perform a performance evaluation using a real-world dataset obtained from a weblog crawl.