Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.

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.

A device implementing the subject system may include at least one processor configured to obtain a first preference profile corresponding to a first user, a second preference profile corresponding to a second user, and a group preference profile corresponding to a user group that includes the first and second users. The at least one processor may be further configured to generate an aggregate preference profile based at least in part on the first preference profile, the second preference profile, and the group preference profile and to identify content items based at least in part on the aggregate preference profile. The at least one processor may be further configured to rank the content items and provide, for display on a content output device, at least one indication of at least one of the ranked content items as a recommendation for the user group.

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.

An electronic device generates a respective user queue for each user of a plurality of users participating in a shared listening session. While providing a first media content item for playback, the device receives a second request, from a first user, to add a second media content item to the shared playback queue and updates the respective user queue for the first user. After receiving the second request, the electronic device receives a third request, from a second user, to add a third media content item to the shared playback queue and updates the respective user queue for the second user. The electronic device updates the shared playback queue using the respective user queues of the first user and the second user, including positioning the third media content item in an order of the shared playback queue to be played back before the second media content item.

Media content search results ranked by popularity is described. In embodiment(s), a search request for television media content can be initiated by a viewer, and television media content that is relevant to the search request can be identified. The relevant television media content can then be ranked based on a popularity rating and the relevant television media content can be displayed in an ordered list that is ordered by popularity rankings.

Systems and methods are described herein for recommending content restrictions to a user based on chatter in a social network of the user. The system analyzes chatter in the social network to identify a correlation between what is posted by users and the content that the users are posting about. The system stores a mapping between chatter and expected attributes of the content referenced by the chatter. The system will determine whether to block the content when an expected attribute is associated with a content restriction.

Examples of the present disclosure describe systems and methods of dynamically modifying execution environment for varying data or executables. The data or executables may be a multimedia file and the execution environment may be multimedia playback settings. In aspects, content comprising various media items may be received by a client device. The media items may be evaluated to determine a set of characteristics for the media items. The set of characteristics may be compared to a user's preferred configuration settings or previously-played media items. Based on the comparison, the set of characteristics may be altered to be consistent with the preferred configuration settings (e.g., a modified set of characteristics may be estimated). The modified media items may then be played under known playback settings. In aspects, the media item under consideration may be played as is using an estimated playback configuration to enable a user-preferable playback experience.

This disclosure relates to digital video analysis. In one aspect, a method includes receiving data indicating one or more seed video groups that each include one or more seed videos. Data indicating one or more keywords is received. A set of candidate video groups that each include one or more candidate videos is identifier. For each candidate video group in the set of candidate video groups a co-interaction score and a topicality score are determined. A subset of the candidate videos groups is selected based on the co-interaction score and the topicality score of each candidate video group. Data indicating the subset of candidate video groups is provided for presentation.

A method in an electronic device includes identifying an authorized user of the electronic device being present within an environment of the electronic device. The method also includes identifying at least one other person also being present within the environment of the electronic device. The method then includes selecting, with one or more processors of the electronic device, content previously partially consumed by the authorized user and the at least one other person and presenting, with the one or more processors, the content on a user interface of the electronic device.