Abstract: Systems and methods for serving dynamic content (such as advertisements) in response to dynamic content requests are presented. More particularly, systems and methods for serving dynamic content from dynamic content portfolios directed to trending events and/or topics are presented. Upon receiving a dynamic content request, content classifications corresponding to the requested, published content (i.e., the requested content) that included reserved dynamic content space which initiated the dynamic content request are determined. A current trend is identified in a content classification when the dynamic content request volume for the current trend period exceeds the standard/expected dynamic content request volume for the current trend period by at least a threshold amount. When a trend is detected, candidate content portfolios directed to the trending content classification are selected from a set trending content portfolios.

Abstract: Systems and methods for serving dynamic content (such as advertisements) in response to dynamic content requests are presented. More particularly, systems and methods for serving dynamic content from dynamic content portfolios directed to trending events and/or topics are presented. Upon receiving a dynamic content request, content classifications corresponding to the requested, published content (i.e., the requested content) that included reserved dynamic content space which initiated the dynamic content request are determined. A current trend is identified in a content classification when the dynamic content request volume for the current trend period exceeds the standard/expected dynamic content request volume for the current trend period by at least a threshold amount. When a trend is detected, candidate content portfolios directed to the trending content classification are selected from a set trending content portfolios.

Abstract: Systems, methods, and computer-readable storage media are provided for determining configurable auction parameters based upon historic auction information that includes identities of auction participants or bidders. Such configurable auction parameters specify values to be applied for particular auction parameters (e.g., floor price), at the time of an auction for available advertising space, when one or more of the auction participants are associated with specified identities. Further provided are systems and methods for, at the time of an auction for available advertising space, determining auction parameter values in accordance with configurable auction parameters.

Abstract: Methods, computer systems, and computer-readable storage media for modifying an advertising bid for delivering behaviorally-targeted advertising based on affinity scores are provided. A set of affinity scores associated with users belonging to a behavioral targeting segment is received. A bid adjustment factor is dynamically calculated based on a first affinity score of the set of affinity scores; the first affinity score is associated with a first user of the set of users. The bid adjustment factor is used to modify the advertising bid such that the bid reflects an affinity of the first user for the topic associated with the behavioral targeting segment.

Abstract: Systems and method are provided for conducting an auction to match an advertising payload to an available advertising impression. In the auction, a dynamic floor price is provided. The dynamic floor price provides a way for a publisher to incorporate the internal value of the available impression into the auction process, while still allowing the publisher to maximize the value of the available impression by exposing the impression to bids from outside parties. The dynamic floor price can be calculated based on both internal valuations of an available impression as well as external valuations derived from feedback from prior auctions. To facilitate determination of a dynamic floor prices, advertisers and available impressions can be categorized into various segments.

Abstract: Systems and methods are provided for evaluating candidate orders relative to available inventory, such as evaluating candidate orders of advertising impressions relative to an estimated available inventor of advertising impressions. The evaluation can include matching orders with available inventors based on linear programs that include objective functions and constraints. At least one objective function can include multiple slack variables.

Abstract: Systems and method can be provided for selecting advertising payloads for display in an available advertising impression location. The advertising payloads can be selected based on an auction between various types of hosted and third party campaigns, including hosted reserved advertising campaigns and hosted non-reserved advertising campaigns. The rules of the auction can be set and/or updated over time to allow hosted campaigns to meet desired goals, such as delivering a minimum number of impressions or spending an expect budget amount.

Abstract: Computer-readable media for determining whether to accept a candidate order from a content provider, or advertiser, to display a particular number of advertisements within a specified time segment are provided. Initially, the content provider may include placement criteria that, among other things, identify a leaf node at which impressions of the advertisement are expected to be rendered. Generally, the leaf node refers to a location within a topic graph that describes inventory that is permissible to allocate to satisfy the candidate order. To perform the determination, the inventory of impressions available for accommodating the candidate order and a log of booked orders scheduled to be placed within the time segment are identified. Linear programs are then utilized to determine whether the estimated inventory that satisfies the placement criteria is available by predictively placing the booked orders at the estimated inventory. If estimated inventory remains available, the candidate order is accepted.

Abstract: Computer-readable media for determining whether to accept a candidate order from a content provider, or advertiser, to display a particular number of advertisements within a specified time segment are provided. Initially, the content provider may include placement criteria that, among other things, identify a leaf node at which impressions of the advertisement are expected to be rendered. Generally, the leaf node refers to a location within a topic graph that describes inventory that is permissible to allocate to satisfy the candidate order. To perform the determination, the inventory of impressions available for accommodating the candidate order and a log of booked orders scheduled to be placed within the time segment are identified. Linear programs are then utilized to determine whether the estimated inventory that satisfies the placement criteria is available by predictively placing the booked orders at the estimated inventory. If estimated inventory remains available, the candidate order is accepted.