Investigation and application of Personalizing Recommender Systems based on ALIDATA DISCOVERY

To aid in the decision-making process, recommender systems use the available data on the items themselves. Personalized recommender systems subsequently use this input data, and convert it to an output in the form of ordered lists or scores of items in which a user might be interested. These lists or scores are the final result the user will be presented with, and their goal is to assist the user in the decision-making process. The application of recommender systems outlined was just a small introduction to the possibilities of the extension. Recommender systems became essential in an information- and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time.