Abstract: The present invention provides a method and a system for providing targeted advertisements to a user. The method includes determining a set of signals corresponding to at least one online activity associated with the user and then analyzing the set of signals to identify one or more user interests. The set of signals includes at least one of a share signal, a view signal, a search signal, and a click signal. Further, the method includes tagging the user with at least one ad exchange cookie based on the identified user interests and an available advertisement pool. Lastly, the method includes displaying, by an advertisement network, an advertisement to the user based on the ad exchange cookie.

Abstract: An improved system and method for online advertising driven by predicting user interest is provided. An advertising demand engine may be provided for selecting advertisements to be served to a user for display with requested content. An advertisement may be correlated to an advertisement previously selected by a user or by other users in the user's segment. An advertising correlation engine may be provided for correlating an advertisement to another advertisement using collaborative filtering, an advertising clustering engine may be provided for clustering correlated advertisements using item-based collaborative filtering, and a user correlation engine may be provided for segmenting users by selected advertisements and creating a cluster of advertisements associated with each cluster of users. Correlated advertisements that are selected may be allocated web page placements and then served to a user for display with requested content.

Abstract: An apparatus and method for determining a solution to a problem where paths in at least one pursuit associated with the problem are traversed to generate one or more decision paths by processing one or more nodes in a given one of the paths, including: evaluating a premise if the node is a premise node, and passing the premise node if the premise passes, and noting a conclusion if the node is a conclusion node, withdrawing any previous conclusions that are not associated with a parent node of the conclusion, and passing the conclusion node; processing a child node if the current node is not a leaf node and is a node that passes; terminating a decision path if the child node cannot be processed, including asserting all noted, but non-withdrawn conclusions as a solution to the problem; and generating another decision path.

Abstract: In a distributed system, a method and system for negotiating a multi-category Quality-of-Service (QoS) agreement between a client and a server includes a client agent enabled to calculate an expected utility to a client of multiple multi-category QoS specifications. The client agent obtains the QoS specifications by transmitting a QoS specification request to a server agent or a broker. The expected utility calculation, based on a probabilistic estimate of QoS levels included in the QoS specifications, enables the client agent to distinguish the QoS specifications of greater value from those of lesser value. The client agent selects at least one of the QoS specifications to be included into an offer for a QoS agreement based on the expected utility calculation. In a preferred embodiment, the client agent selects the QoS specifications determined to be most valuable to the client.