Techniques to predict attributes for a user or user group are described. User profiles and connections to other users are analyzed to determine user groups such as a household. Clusters are formed based on matching attributes of similar user groups. Available databases are queried using the user group identifiers to obtain a group-wide attribute value, e.g., household income, for a subset of user groups of a cluster. The obtained values are used to predict missing attribute values that were not found in the database, after adjusting for variations between user groups, such as a number of users in the group. User/group profiles are updated with the predicted attribute values. The predicted attribute values are utilized to customize content delivery to users.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.