1. A hybrid recommender system for delivering content that fits a user preferences, implementing a process comprising the steps of: a. manually defining one or more initial stereotypes by an expert; b. creating an affinity vector of stereotypes relating to each specific user who registers onto the system, thereby creating a specific profile for each user, wherein said affinity vector for each user is created according to her response to a set of questions, where each question compares several representative items, and a form, based on a decision tree and specifying said user's interests, and wherein said affinity vector is saved in a database; c. generating recommendations for a specific user according to said initial stereotype and said affinity vector of stereotypes, wherein said recommendations are generated using a matching engine that computes the relevance of an item to the user, thereby creating a recommendation list of items to incorporate into the database; d. receiving feedback from user regarding specific items picked by him, wherein said feedback can be either positive or negative; and e. updating said affinity vector of stereotypes and said recommendation list of items saving them in said database; wherein the content delivered to the user contains a predefined number of the most relevant items to her according to said recommendation list.