Before revision:  This entails not only predicting accurate preference estimates  as classical recommenders do, but also defining adequate fusion processes for aggregating user-to-user preferential information.
After revision:  This entails not only predicting accurate preference estimates as classical recommenders do, but also calculating mutual compatibility between (pairs of) users, typically by applying fusion processes on unilateral user-to-user preferential information.