A processing system may identify a context of a user selection within a content, identify a sequence of known user interactions with the content based on the context, modify the content to include at least one custom selection based on the sequence of known user interactions that is identified, and present the content that is modified to a user.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

Example methods and apparatus to replicate panelists using a local minimum solution of an integer least squares problem are disclosed. An example apparatus includes memory; and processor circuitry to execute computer readable instructions to: determine weight adjustments based on a ratio of (A) a first dot product of (i) attribute data and (ii) a difference between first aggregate panelist data and second seed panelist data, and (B) a second dot product of the attribute data and the attribute data, the first aggregate panelist data based on the attribute data, the attribute data corresponding to a seed panel; determine weight estimates based on the weight adjustments, the weight estimates to replicate respective seed panelists in the seed panel; and replicate respective ones of the seed panelists based on the weight estimates to generate a synthetic audience representative of return path data reported by a plurality of media devices.

Systems and methods for determining, based on recommendations provided by users that have consumed a media asset, which consumption options may be configured on a media device such that when configured enhance the user viewing experience for a specific media asset. The method includes accessing comments posted by other users that have consumed the media asset. The comments are analyzed to determine a consumption option recommendation. If the number of comments meet a threshold value, then the system either automatically configures the media device or configures the media device upon user approval with the recommended consumption option. The recommendation to configure a consumption option on the media device is made only if the recommendation is supported by the media device. The system also detects through audio and image analysis which users are consuming the media asset and accordingly configures the consumption options to their preferences.

In some embodiments, a method inputs a sequence of historical behaviors for a plurality of instances of content into a prediction network to generate a sequence of values that model the sequence of historical behaviors. A restriction on an operation performed by the prediction network is based on a characteristic of an viewing behavior. A sequence of attention scores is generated based on a similarity of a current behavior for a first instance of content to respective instances of historical behaviors in the sequence of historical behaviors. The method adjusts respective values based on corresponding attention scores to generate an adjusted sequence of values. The adjusted sequence of features are sampled to generate an output from the prediction network that models the sequence of historical behaviors based on the current behavior. The output for determining a prediction if the current behavior is indicative of the viewing behavior.

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.

The disclosed embodiments relate to a behavior control method and apparatus for a virtual live streaming character. In an embodiment, a method comprises: acquiring viewer behavior data of a live streaming service, and determining atmosphere data of the live streaming service according to the viewer behavior data; acquiring scenario data of the live streaming service, using the viewer behavior data, the atmosphere data, and the scenario data as behavior decision input data, and performing multi-modal feedback behavior decision-making based on the behavior decision input data to acquire a behavior control instruction; and performing a corresponding behavior operation according to the behavior control instruction. The disclosed embodiments combine existing knowledge and newly added behavior, achieves targeted behavior decision-making and control, improves the accuracy of virtual human behavior control, and considers both flexibility and comprehensiveness, thereby greatly improving the quality of virtual human behavior, and improving viewing experience of users.

Systems and methods for efficiently loading interactive media guide data based on user history are provided. A plurality of asset descriptions associated with a first media asset is received. A second media asset related to the first media asset is identified. An asset description is selected form the plurality of asset descriptions based on user history of accessing the second media asset. Data for the first media asset is loaded. Data for the first media asset comprises the selected asset description.

Systems and methods are described for a media guidance application (e.g., implemented on a user device) that explains sports terminology to a user accessing content that corresponds to a sporting event. The media guidance application may detect terms used in the content, determine the terms are unique to the sport, and display definitions and explanations alongside the terms.

Systems and methods are provided herein for flexibly using trending topics as parameters for recommending media assets that are related to a viewed media asset. A media guidance application may determine that a user has viewed a media asset. The media guidance may identify a plurality of attributes corresponding to the viewed media asset and determine that a respective attribute of the plurality of attributes matches a trending topic. The media guidance application may update a set of weightings corresponding to the plurality of attributes by increasing a weighting corresponding to the respective attribute and adjust a recommendation for a media asset different from the viewed media asset based on the updated set of weightings. The media guidance application may generate for display the recommendation of the media asset different from the viewed media asset.