Abstract: A method and system for utilizing user information to provide a network address. The method collects user information about a user from at least one electronic device that is associated with the user. In addition, a network address activity pattern is identified based on a combination of a network browsing activity and the user information. A list of network addresses that have been viewed in context of the activity pattern is then accessed. At least one network address on the list of network addresses is scored. Then, the at least one network address having a highest score is provided to the user.

Abstract: A method and system for improving targeted data delivery. One method accesses a user profile based on information about a user from at least one electronic device from a set of electronic devices that is associated with the user. A targeted data offer is received, wherein the targeted data offer defines selection criteria for targeted user profiles. A determination is made as to whether the user profile satisfies the selection criteria. A non-user selected relevancy criteria based on the user profile is then utilized to characterize the targeted data offer.

Abstract: A method and apparatus is used to forecast a goal in a principal-agent environment. The forecast includes providing an agent with a menu of incentive contracts having a sliding-scale between a fixed compensation portion and a at-risk compensation portion that depends on the agent meeting the goal, requesting the agent select the incentive contract combining the fixed compensation portion with the at-risk compensation portion in accordance with the agents private knowledge of the goal at the time of the selection, and forecasting the likelihood of the goal occuring based on the incentive contract selected by the agent using the agent's private knowledge.

Abstract: A method and apparatus for clustering documents from a set of documents includes issuing a request from an electronic device for documents which are relevant to the request. The documents believed to meet the request requirements are identified and the documents are thereafter supplied to a document clustering system. The document clustering system forms clusters of the documents by operation of the clustering system. A robustness criterion, by analogy with self-assembled physical systems, is applied to select the best, most robust, clustering. Robust clusters have a higher designability feature than other clusters which are generated where designability refers to a number of ways a particular cluster can be formed.