Systems and methods for generating a content item based on a difference between a user confidence score and a confidence score are disclosed. For example, a system generates for output a first content item. While the first content item is being outputted, the system receives user data via sensors of a device. The system determines a user confidence score based on the user data and metadata of the first content item. The user confidence score indicates a user's perceived probability of an event occurring in the future. The system calculates a prediction score which estimates the likelihood of the event occurring in the future. In response to determining that the difference between the user confidence score and the prediction score exceeds a threshold, the system selects a second content item related to the event and generates for output a recommendation comprising an identifier of the second content item.

In some embodiments, a method receives an supplemental content placement and a context associated with a request for supplemental content to be displayed for the supplemental content placement. A first value is generated based on the context using a prediction network for a platform. The method determines probabilities for a plurality of types of request actions based on the context. Then, a threshold for the supplemental content placement is calculated based on the first value and the probabilities for the plurality of types of request actions. The method submits the threshold to a platform in a request for the platform to submit a second value for the supplemental content placement.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform probabilistic modeling for anonymized data integration and measurement of sparse and weakly-labeled datasets are disclosed. An apparatus includes a training controller to train a neural network to produce a trained neural network to output model parameters of a probability model, a model evaluator to execute the trained neural network on input data specifying a time of day, a media source, and at least one feature different from the time of day and the media source to determine one or more first model parameters of the probability model, and a ratings metric generator to evaluate the probability model based on input census data to determine a ratings metric corresponding to the time of day, the media source, and the at least one feature, the probability model configured with the one or more first model parameters.

An example to predict demographics for impressions includes a prediction manager to: determine that first demographic data corresponding to a first database proprietor subscriber does not match second demographic data corresponding to a media delivery device, both the first and second demographic data corresponding to an impression; obtain third demographic data corresponding to an Internet protocol address, the third demographic data obtained from a second database proprietor; and generate matched demographic data based on comparing the third demographic data to the first demographic data; and a modeler to generate a prediction model based on the matched demographic data, the prediction model to predict fourth demographic data for the impression.

Systems and methods for providing fast forwarding recommendations based on the user's consumption history are disclosed. The consumption history includes data relating to attributes that were previously rewinded and watched and those that were skipped and forwarded. It also includes scores for attributes that were present and absent in a portion that was previously rewinded or forwarded. A score is assigned to the attributes and used for determining a consumption pattern. If the consumption pattern indicates that the user previously rewinded and watched the attribute, then a recommendation not to skip an upcoming portion that includes the attribute is provided. A graphical timeline that depicts the amount of time saved by skipping the portion of the media asset with the attribute is also provided.

A method of recommending media items to a user is provided. The method includes receiving historical data for a user of a media providing service. The historical data indicates past interactions of the user with media items. The method includes generating a model of the user. The model includes a first set of parameters, each of the first set of parameters quantifying a predicted latent preference of the user for a respective media item provided by the media providing service. The method includes evaluating the predicted latent preferences of the user for the respective media items against the historical data indicating the past interactions of the user with the media items provided by the media providing service. The method includes selecting a recommender system from a plurality of recommender systems using the model of the user, including the first set of parameters. The method includes providing a media item to a second user using the selected recommender system.

Methods, apparatus, systems and articles of manufacture to assign viewers to media meter data are disclosed. An apparatus includes memory, and a processor to execute instructions to: determine first probabilities for first panelists in a first household based on a first number of minutes of first media presented by a first media presentation device monitored by a first meter, determine second probabilities for second panelists in a plurality of second households based on a second number of minutes of second media presented by second media presentation devices monitored by a plurality of second meters, compare the first probabilities and the second probabilities to identify a candidate household from the plurality of second households to associated with the first household, and impute respective portions of the first number of minutes to corresponding ones of the first panelists when monitored behavior of the candidate household matches monitored behavior of the first household.

Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.

Systems and methods are described herein for curating content that follows a narrative structure. A narrative structure comprises narrative portions that have a defined order. Signature analysis of known content that follows the narrative structure is used to train machine learning models for the narrative structure and the narrative portions that make up the narrative structure. Signature analysis of candidate content segments, along with machine learning models for the narrative portions, are used to identify candidate content segments that match the respective narrative portions. A candidate playlist is generated of the identified candidate content segments in the defined order. In one embodiment, the machine learning model for the narrative structure is used to validate the generated playlist.

Systems and methods for generating a content item based on a difference between a user confidence score and a confidence score are disclosed. For example, a system generates for output a first content item. While the first content item is being outputted, the system receives user data via sensors of a device. The system determines a user confidence score based on the user data and metadata of the first content item. The user confidence score indicates a user's perceived probability of an event occurring in the future. The system calculates a prediction score which estimates the likelihood of the event occurring in the future. In response to determining that the difference between the user confidence score and the prediction score exceeds a threshold, the system selects a second content item related to the event and generates for output a recommendation comprising an identifier of the second content item.