and personal agents trained for the target customer using rule induction and information retrieval
(TFIDF) techniques. New work is underway to explore the value to customers of having access to agents trained for other customers
(i.e., those that try to mimic others but do so by building a preference model based on content features and therefore can rate all
items). Adding other machine learning systems to recommender systems may increase the accuracy of the recommender system,
especially in cases where few humans have tried the products in question.