The data amount on the web is serious problem for the common user. The existence of information is not so relevant, when there is no one who can access or find this information in acceptable time. One of the most relevant sources of information over the web is presented by news portals (,, etc.). Most users prefer large renowned news metaportals. They include thousands of daily added news from the whole world and there is no chance to access them in a fast and comfortable way for every user. The only way to help the user is to personalize large amount of information and reduce it to an acceptable amount.

Our method for similarity computation compresses article information value to short vectors, which are used for fast similarity computation over the specific articles time-window. This vector represents article in an effective way, so there is no need to store whole articles. Proposed method expects pre-processed article as an input and produces vector representation usually no longer than 30 words. Then these vectors can be easily used for similarity computations or we can use them in special structures for recommendation. Fast similarity estimation plays the critical role in the high changing domains as news portals are. It is necessary to process new article as fast as possible and start to this article recommendation, because of the high information value degradation.