Abstract: Methods and systems are described herein for predictive adjusted bidding for electronic advertisements. A bid determination computing device receives, from an ad exchange computing device, a bid request for an available underlying impression opportunity. The bid determination device determines an opportunity value estimate for the impression opportunity based upon the bid request, and determines a probability of a submission of one or more other bid requests for the same impression opportunity by the same ad exchange device or by one or more other ad exchange devices. When the determined probability indicates a submission of one or more other bid requests for the same impression opportunity, the bid determination device adjusts the opportunity value estimate for the impression opportunity based upon the determined probability and transmits the opportunity value estimate to the ad exchange device in response to the bid request.

Abstract: Methods and systems are described herein for incremental bidding for electronic advertisements. A computing device generates, for a user during a first time period, first randomized bids for available impression opportunities, the first randomized bids based upon an estimated value and a first random factor and using a context of bid requests. The computing device transmits the first randomized bids to a remote device. The computing device determines whether interaction events associated with impression opportunities occurred. The computing device estimates second randomized bids based upon an estimated value and a second random factor and using the context. The computing device estimates a relative incrementality of a bidding policy based upon the second randomized bids over a bidding policy based upon the first randomized bids, and optimizes the bidding policy based upon second randomized bids. The computing device changes a production bidding policy based upon the optimized policy.

Abstract: Methods and systems are described herein for incremental bidding for electronic advertisements. A computing device generates, for a user during a first time period, first randomized bids for available impression opportunities, the first randomized bids based upon an estimated value and a first random factor and using a context of bid requests. The computing device transmits the first randomized bids to a remote device. The computing device determines whether interaction events associated with impression opportunities occurred. The computing device estimates second randomized bids based upon an estimated value and a second random factor and using the context. The computing device estimates a relative incrementality of a bidding policy based upon the second randomized bids over a bidding policy based upon the first randomized bids, and optimizes the bidding policy based upon second randomized bids. The computing device changes a production bidding policy based upon the optimized policy.

Abstract: Methods and systems are described herein for predictive adjusted bidding for electronic advertisements. A bid determination computing device receives, from an ad exchange computing device, a bid request for an available underlying impression opportunity. The bid determination device determines an opportunity value estimate for the impression opportunity based upon the bid request, and determines a probability of a submission of one or more other bid requests for the same impression opportunity by the same ad exchange device or by one or more other ad exchange devices. When the determined probability indicates a submission of one or more other bid requests for the same impression opportunity, the bid determination device adjusts the opportunity value estimate for the impression opportunity based upon the determined probability and transmits the opportunity value estimate to the ad exchange device in response to the bid request.

Abstract: Methods and systems are described herein for predictive attribution-adjusted bidding for electronic advertisements. A bid determination computing device receives a bid request for an available impression opportunity on a website, and determines an initial opportunity value estimate for the available impression opportunity. The bid determination computing device identifies one or more interaction events associated with one or more prior impression opportunities for which the bid determination computing device submitted a winning bid, and determines a probability that a target outcome is attributable to at least one of the interaction events. The bid determination computing device adjusts the initial opportunity value estimate for the available impression opportunity based upon the determined probability, and transmits the adjusted opportunity value estimate to a remote computing device in response to the bid request.

Abstract: A method and apparatus is provided for determining related keywords to narrow a query, and/or for categorizing a query. A keyword graph connects keyword nodes to each other based on degrees of cross-reference indicating how frequently keywords associated with the nodes appear in searches. A domain node representing a category hooks to a category-matching node in the keyword graph. Based at least in part on a degree of cross-reference between another node and the category-matching node, the domain node hooks to the other node. Alternately, the domain node hooks to nodes that match user-identified keywords. At query time, the query is categorized by the domain node closest to a node matching the query. Keywords related to the category may be determined from the nodes that are hooked to the domain node. The related keywords can be used to narrow a search or expand the metadata of a document.

Abstract: A method and apparatus is provided for determining related keywords to narrow a query, and/or for categorizing a query. A keyword graph connects keyword nodes to each other based on degrees of cross-reference indicating how frequently keywords associated with the nodes appear in searches. A domain node representing a category hooks to a category-matching node in the keyword graph. Based at least in part on a degree of cross-reference between another node and the category-matching node, the domain node hooks to the other node. Alternately, the domain node hooks to nodes that match user-identified keywords. At query time, the query is categorized by the domain node closest to a node matching the query. Keywords related to the category may be determined from the nodes that are hooked to the domain node. The related keywords can be used to narrow a search or expand the metadata of a document.