The purpose of this paper is to describe our approach to Tag Recommendation Task during ECML PKDD 2009 Challenge. The organizers supplied a training set of tagged webpages and publications from BibSonomy portal. Our goal was to build a model which can predict tags for new users bookmarking digital resources. Our strategy was based on an assumption that users tend to tag the same resources in various systems. Therefore, have we developed a tracking engine, which was adjusted to the profile of

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... Sonomy users in selection of RSS feeds and utilized the training data to optimize the list of tracked URLs. We had over 90 days to collect the data from the feeds, but this period did not overlap with the dates of posts from the training set. As a result we had to set manually parameters responsible for a trade-off between recall and accuracy of the model. We stored all downloaded feed entries in a searching engine. The recommendation was based on tags attached to the documents retrieved from the engine by means of typical information retrieval query.