Abstract: A front-end system collects user attribute value data and organizes the data into one or more training data sets and one or more test data sets. The front-end system provides at least some of the test data sets to an input layer of a machine learning system. Within the machine learning system, one or more predictive models are constructed. At an output layer, the predictive models provide output data that includes at least a value indicative of whether a user will upgrade service levels based at least in part on the attribute values logically associated with the respective user. A back-end system generates upgrade offers for subsequent communication to those users identified as being likely to upgrade.

Abstract: Websites operated by relationship building service providers collect information from users for use in identifying candidate matches for a user. At least a portion of the information may include freeform text entered by the user to identify the user's interests. The freeform text entered by users may be preprocessed and a user interest group model formed using a generative Natural Language Process that is applied to determine user interest groups from the freeform text. Such user interest groups identify related words based on the frequency of their occurrence and relationship within the freeform text. Individual user interest group membership vectors for each user included in a number of users may be determined using the user interest group model. At least one compatibility aspect between users may then be assessed based on the similarity or distance between the user interest group membership vectors associated with each user.