The present disclosure describes techniques of implementing a collaborative advertising mechanism. The techniques comprise receiving a plurality of pieces of content associated with a mission event from a first plurality of client computing devices associated with a first plurality of users, evaluating the plurality of pieces of content based on one or more predetermined rules of the mission event and information indicative of viewer reactions to the plurality of pieces of content, determining at least one subset of the plurality of pieces of content based on a plurality of evaluation results corresponding to the plurality of pieces of content and user input from a client computing device associated with one of a second plurality of users, and distributing the at least one subset of the plurality of pieces of content.

An example apparatus includes an audience size calculator circuitry to determine a first census-level audience size, and an impression count calculator circuitry to determine a first census-level impression count. The example apparatus includes a verification controller circuitry to determine whether the first one of the plurality of cross-demographic total census audience parameter values satisfies a first constraint; determine the first one of the plurality of cross-demographic total census impression parameter values satisfies a second constraint based on the first census-level impression count; and when the first constraint is not satisfied or the second constraint is not satisfied: (a) discard the first one of the cross-demographic total census audience parameter values and the first one of the cross-demographic total census impression parameter values, and (b) select a second one of the cross-demographic total census audience parameter values and a second one of the cross-demographic total census impression parameter values.

A method includes accessing web browsing history for a plurality of users, generating embedding vectors based on the web browsing history for websites, and selecting a model configured to receive embedding vectors and output probability of a conversion events. Further, the method includes calculating a probability of a conversion event for the various websites using the model, selecting a subset of websites from the various websites based on websites having associated probabilities greater that a predetermined probability threshold, and receiving an indication that an impression has been displayed to a user when the user visits a website from the subset of websites, obtaining a plurality of conversion rates, each conversion rate is determined for each website from the subset of websites based on a number of conversion events associated with the plurality of visitation events, and updating the model parameters of the model using the obtained plurality of conversion rates.

Systems and methods are described herein for determining user exposures to content, such as content presented by physical objects (e.g., advertisements on billboards). The systems and methods may determine a probability (an exposure probability) that a user has viewed or consumed content, information, or other visual media presented by a physical object, such as a billboard, vehicle, sign, or other structure, and provide the determined probability to various attribution systems, such as systems that attribute user engagements (e.g., store visits, app downloads, website visits, product purchases, and so on) to earlier content exposures.

A method including training a logistic regression model to create a trained model to provide probabilities of users clicking on emails of one or more email campaigns within each of multiple different time periods. Input predictor variables of the logistic regression model include (i) user feature data including personal user features and online activity history for users in the multiple different time periods and (ii) email feature data including sent times and item category data for multiple different emails in the one or more email campaigns. Output dependent variables of the logistic regression model include responses by the users to the one or more email campaigns. The method also includes triggering sending a first email of the one or more email campaigns to a first user of the users at a selected time period based at least in part on the trained model. Other embodiments are disclosed.

A computer-implemented method for efficiently estimating the number of unique elements in a collection of elements comprises generating, via hash logic, hash values for each element of the collection of elements. The method further comprises specifying, in a sketch-frequency table, a set of discrete statistical values associated with the hash values and, for each discrete statistical value of the set of discrete statistical values, information indicative of a frequency at which binary representations of the hash values are associated with the discrete statistical value. The cardinality of the collection of elements is estimated based on the sketch-frequency table.

A system and method of advertising for use on an internet and/or digital networking capable device, wherein the system allows advertisers to use a loading space generated during the initiation of a process on the device to post any media and/or advertising content during the time between when a program or web page is requested and when it actually loads.

Using various embodiment, methods and systems to implement an objective based advertisement placement platform are described. In one embodiment, a method and system to display advertisements in three dimensional (3D) online environment based on an objective of an advertiser is disclosed. A computing device receives the objective of the advertiser. In one embodiment, the advertiser objective includes determining when to stop displaying a branded smart object (BSO) to a user in the 3D online environment, the objective including an engagement rule. The computing device can further determine whether the advertiser's objective has been achieved by a user, the determining including evaluating a user engagement score (UES) and comparing the UES with an advertiser's engagement rule. If the advertiser's objective is achieved (or met) by the user, the BSO is not displayed to the user for a predetermined period of time.

In one embodiment, a computer data store comprises a plurality of branded 3D digital assets that can be displayed in an online environment, where a branded 3D digital asset can transmit engagement metric data based on user interaction or viewability with the branded 3D digital asset, displayed on a client machine. The client machine is configured to determine a proportion of the branded 3D digital asset displayed on screen of a graphical user interface of the client machine, and obtain a percentage of the screen of the graphical user interface that the branded 3D digital asset is covering using a screen bounding function of a 3D engine of the online environment to generate engagement metric data. A computer server is configured to receive an objective related to a placement of the branded 3D digital asset, the objective determining when to display the branded 3D digital asset. The objective further including an engagement rule. The branded 3D digital asset is retrieved from the computer data store and displayed in the online environment. The server then determines whether the objective has been achieved by the user, and if so, prevents display of the branded 3D digital asset for a predetermined period of time. In another embodiment, the client computer system can be further configured to determine whether the 3D digital asset is drawn on a culling mask of a camera in the online environment, the 3D digital asset comprising a collidable mesh, draw a line between the camera and the 3D digital asset using a ray casting based technique, and determine that the line collides with the collidable mesh of the 3D digital asset. The client computer machine obtains the percentage of the screen of the graphical user interface when it is determined that the line collides with the collidable mesh of the 3D branded digital asset.

High conversion rate content can be displayed with primary content from one or more publishers in order to determine whether the content is being displayed to human users or provided to automated processes such as robots. Convertible content such as advertising will generally result in conversions or other actions within an expected range of occurrences. Convertible content performing significantly below the range can be indicative of robotic traffic. Such determinations can be difficult for publishers with low volume traffic, however, as there may not be sufficient data to make an accurate determination. For such publishers, or users viewing content for such publishers, high conversion rate content can be displayed that will allow such determinations to be made with fewer data points. The rates can be used to determine robotic users, which can be blocked, as well as to determine poorly performing placements of the content by the publishers.