In this paper a novel article ranking method called NectaRSS is introduced. The system
recommends incoming articles, which we will designate as newsitems, to users based on their past
choices. User preferences are automatically acquired, avoiding explicit feedback, and ranking is
based on those preferences distilled to a user profile. NectaRSS uses the well-known vector space
model for user profiles and new documents, and compares them using information retrieval
techniques, but introduces a novel method for user profile creation and adaptation from users’ past
choices. The efficiency of the proposed method has been tested by embedding it into an intelligent
aggregator (RSS feed reader) which has been used by different and heterogeneous users. Besides, this
paper proves that the ranking of newsitems yielded by NectaRSS improves its quality with user’s
choices, and its superiority over other algorithms that use a different information representation
method.