Methods, apparatus, systems and articles of manufacture to identify and triage digital ad ratings data quality issues are disclosed. An example apparatus includes score calculation circuitry to: generate one or more aggregate factor scores based on aggregate data from a first impression data point; generate one or more daily factor scores based on daily data from the from impression data point; normalize the one or more aggregate factor scores based on aggregate factor scores of at least a second data impression point; normalize the one or more daily factor scores based on daily factor scores of at least a second data impression point; calculate a final weight score for the first impression data point using the aggregate factor scores and the daily factor scores for the first impression data point; and flag the final weight score if it does not satisfy a threshold score.

The present disclosure describes a content consumption monitor (CCM) that determines surges in content consumption based on changes in content consumptions scores. The CCM determines the content consumptions scores for domains and/or organizations (orgs) based on session events generated by different devices/users from the org and/or domain, a number of events generated by the org/domain, content and/or user interactions with the content indicated by the events, relevancy scores of the content to one or more topics, and/or other criteria. The CCM detects surges in consumption or interest in a topic for the domain/org when the consumption score reaches a threshold and/or within a period of time. The CCM may adjust the consumption score based on the changes in the relevancy, number of events and/or the number of users over different time periods. Other embodiments may be described and/or claimed.

Methods, systems, and media for estimating the causal effect of different content exposure levels are provided.

Methods, systems, and computer storage media are provided for tracking an advertisement based on the advertisement's context. When an ad event is received on a client-computing device, a single URL is determined to display an item and track a context of the ad event. A first parameter related to the ad event is encoded as a HTTP header, and a second parameter related to tracking the ad event is encoded as a query parameter appended to the URL. The URL with the HTTP header is called, causing a domain server named in the URL to extract the first parameter from the HTTP header and the second parameter from the query parameter in order to determine the context of the ad. The domain server asynchronously requests tracking of the ad based on the context. Additionally, content for a landing page is received from the domain server.

A mobile advertising system includes a non-transitory computer readable configured to store instructions thereon; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for receiving gaze data from a viewing vehicle. The processor is configured to execute the instructions for receiving location information from an advertising vehicle. The processor is configured to execute the instructions for correlating the gaze data with the location information to determine whether the gaze data indicates viewing of an advertisement attached to the advertising vehicle. The processor is configured to execute the instructions for updating a histogram based on the correlation between the gaze data and the location information. The processor is configured to execute the instructions for generating a travel plan for increasing advertising effectiveness for the advertisement. The processor is configured to execute the instructions for transmitting the travel plan to the advertising vehicle.

A mobile advertising system includes a non-transitory computer readable configured to store instructions thereon; and a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for receiving gaze data from a viewing vehicle. The processor is configured to execute the instructions for receiving location information from an advertising vehicle. The processor is configured to execute the instructions for correlating the gaze data with the location information to determine whether the gaze data indicates viewing of an advertisement attached to the advertising vehicle. The processor is configured to execute the instructions for updating a histogram based on the correlation between the gaze data and the location information. The processor is configured to execute the instructions for generating a travel plan for increasing advertising effectiveness for the advertisement. The processor is configured to execute the instructions for transmitting the travel plan to the advertising vehicle.

To provide a platform, a system, a method, and a program each adapted to predict an information distribution medium which attracts interest of a plurality of users. A platform according to the present disclosure includes: a gathering unit configured to gathering user information items that are used to predict an information distribution medium which attracts interest of a user out of a plurality of the information distribution media; and a prediction unit for predicting the information distribution medium which attracts interest of a plurality of the users at a prescribed time, which is a predetermined point in time ahead of the current time by a predetermined time length, out of the plurality of the information distribution media based on the user information items of the plurality of the users and information about the plurality of the information distribution media from the past up to the current time.

An electronic device includes at least one processor configured to obtain user data associated with a plurality of devices from multiple data sources. The at least one processor is also configured to determine a static weight for each of the plurality of devices based on at least one source of the multiple data sources. The at least one processor is further configured to identify a portion of the plurality of devices that represents the plurality of devices based on the static weight and a dynamic weight. In addition, the at least one processor is configured to determine the dynamic weight for each of the portion of the plurality of devices while the portion of the plurality of devices is identified, where the dynamic weight is based on one or more sources of the multiple data sources.

Systems and methods for dynamically tracking delivery and performance of digital advertising placed on non-personal devices in physical locations and integrating, displaying, and reporting impressions and events in digital advertising systems.

Aspects of the present disclosure relate to machine learning techniques for identifying the incremental impact of different past events on the likelihood that a target outcome will occur. The technology can use a recurrent neural network to analyze two different representations of an event sequence—one in which some particular event occurs, and another in which that particular event does not occur. The incremental impact of that particular event can be determined based on the calculated difference between the probabilities of the target outcome occurring after these two sequences.