Abstract: Data mining for managing marketing resources is disclosed. In one embodiment, a method for managing a marketing campaign includes the following. First, the method provides a data mining engine capable of being trained with training data and capable thereof of performing inference relative to the training data and on future data. Next, the method provides a user database defining observed characteristics of each one of a set of users. The characteristics include at least one of one or more user's attributes, and one or more of the user's preferences. Finally, the data mining engine is trained with a set of training data comprising the user data base, and a predetermined characteristic pertaining to the market campaign is input to the engine, such that, in response to the input, a subset of the users in the database is obtained that have the highest correlation to the characteristic.

Abstract: Retrofitting recommender systems, so that they can scale to large data, is disclosed. The principal notion is to reduce data requirements of existing recommender engines by performing a type of data reduction that minimizes the loss of information given the engine. The reductions covered in this invention are designed to be easily implemented on a database system, and are intended to have minimal impact on an existing implementation of a recommender system. In one embodiment, a method repeats reducing the data by a number of records, until an accuracy threshold or a performance requirement is met. If the accuracy threshold is met first, the method repeats removing a highest-frequency dimension from the data, until the performance requirement is also met. The reduced data is provided to the recommender system, which generates predictions based on the reduced data, and a query.