Abstract: Methods and systems are described herein for risk-adjusted predictive bidding for electronic advertisements. A first computing device receives, from a requesting device loading a webpage, a first request for graphical display source code corresponding to a computerized graphical advertisement display to be inserted into one or more impression opportunities on the webpage. A second computing device coupled to the first computing device transmits a second request to a plurality of third-party computing devices for one or more bids for an impression opportunity of the one or more impression opportunities. The second computing device receives the one or more bids from the third-party computing devices and adjusts at least one of the one or more bids based upon a risk factor received from the third-party computing device that submitted the corresponding bid. The first computing device determines whether to select the impression opportunity based on the adjusted one or more bids.

Abstract: An online system displays a first set of content items to a user of a test group and displays a second set of content items to a user of a control group. The online system presents a poll to each user to evaluate the user's recall of the content item associated with the poll. The online system receives a poll response from each user, which is input, along with a set of features associated each user, into a prediction model. The prediction model enables the online system to determine a poll response prediction of a third user based on a set of features associated with the third user. The poll response prediction enables the online system to determine if it would be effective to present the content item to the third user.

Abstract: One or more embodiments of the present disclosure involve training and utilizing a recall machine learning model to predict recall lift on a per-user basis with respect to digital content items. For example, systems described herein train a recall machine learning model based on poll responses from exposed users and non-exposed users with regard to sample digital content. In particular, the systems described herein train the recall machine learning model to output recall lift scores for a target user based on an assumption that the target user has been exposed to digital content and an assumption that the target user has not been exposed to the digital content. The systems described herein further involve delivering digital content in accordance with the recall lift score.

Abstract: Methods and systems are described herein for risk-adjusted predictive bidding for electronic advertisements. A first computing device receives, from a requesting device loading a webpage, a first request for graphical display source code corresponding to a computerized graphical advertisement display to be inserted into one or more impression opportunities on the webpage. A second computing device coupled to the first computing device transmits a second request to a plurality of third-party computing devices for one or more bids for an impression opportunity of the one or more impression opportunities. The second computing device receives the one or more bids from the third-party computing devices and adjusts at least one of the one or more bids based upon a risk factor received from the third-party computing device that submitted the corresponding bid. The first computing device determines whether to select the impression opportunity based on the adjusted one or more bids.

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.