The paper describes RankFeed a new adaptive method of recommendation that benefits from similarities between searching and recommendation. Concepts such as: the initial ranking, the positive and negative feedback widely used in searching are applied to recommendation in order to enhance its coverage, maintaining high accuracy. There are four principal factors that determine the method s behaviour: the quality document ranking, navigation patterns, textual similarity and the list of recommended pages that have been ignored during the navigation. In the evaluation part, the local site s behaviour of the RankFeed ranking is contrasted with PageRank. Additionally, recommendation behaviour of RankFeed versus other classical approaches is evaluated.

recommendation, information retrieval, Web mining, personalization, adaptive systems, ROSA