Systems, methods, and non-transitory computer-readable media can collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective. A machine learning model is trained based on the training data. A video to be evaluated is received. The video is assigned to a first video quality category of a plurality of video quality categories based on the machine learning model.

According to examples, a system for providing dynamic digital content may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to receive a plurality of data feeds. The processor may further analyze the data feeds to identify values for parameterized variables. A plurality of deep learning (DL) models can be trained to obtain product attribute data from the data feeds. The processor may then identify rules or triggers based on the values of the parameterized variables. The rules and/or triggers cause the processor to dynamically generate or select digital content and transmit the digital content to user communication devices of selected audience.

A play control method includes obtaining a playing state of at least one of a plurality of terminals playing a same video; and controlling a playing progress of at least one of the plurality of terminals when the playing state meets a preset condition. Using the disclosed play control method and apparatus for a plurality of terminals playing a same video, playing synchronization of the plurality of terminals can be maintained under a premise of ensuring that the plurality of terminals do not miss each video segment, so that no communication barrier is resulted due to asynchronous video playing during interactions between users who watch the same video, thus being able to improve user experience.

Aspects of the subject disclosure may include, for example, a system and method for selecting media content for a group of persons located at a venue. The system and method identify a plurality of viewers in a vicinity of a venue that has one or more display devices from location data and extracts a content viewing preference of each viewer from their profile data. The process includes aggregating the content viewing preference of each of the plurality of viewers to generate an aggregated content profile. Based on the aggregated content profile, a list of content is generated. Next, a first group of viewers approaching a viewing range of a display device are sensed by imaging data. Any conflicts between content viewing preferences of the first group of viewers are detected and resolved based on weighting the viewing preferences of the group. Other embodiments are disclosed.

A computer-implemented is disclosed. The method includes: receiving media data of a live media stream; detecting a trigger associated with the media data of the live media stream; in response to detecting the trigger, generating at least one of audio or video overlay content associated with the trigger; and transmitting, to viewer devices, the at least one of audio or video overlay content with the live media stream.

The serving of advertisements with (e.g., on) video documents may be improved in a number of ways. For example, a system may (a) accept information defining at least one ad spot associated with at least one instance of an video document, (b) accept offers to have advertisements served in the ad spot(s), and (c) arbitrate among competing advertisements, using at least the offers, to determine at least one advertisement to be served in that ad spot(s). As another example, a system may (a) accept relevance information for an advertisement, (b) determine at least one video document using the accepted relevance information, (c) present information about the video document(s) to an advertiser associated with the advertisement, and (d) accept, from the advertiser, an offer to have its advertisement served with at least one of the video document(s) accepted. As yet another example, a system may (a) accept relevance information for an video document, (b) determine a plurality of advertisements relevant to the video document using the relevance information and serving constraints of the advertisements, and (c) select at least one of the determined relevant advertisements to be served with the video document. Examples of video documents include video files published on the Internet, television programs, live or recorded talk shows, video-voice mail, segments of an video conversation, etc.

A system for automatically managing the delivery of media assets allocates the media assets to delivery slots of a media delivery servers so that consumers will receive the media assets when they consume digital media programming at times that correspond to the delivery slots. An example is the automated allocation of sponsored videos to television programs airing on a particular afternoon. The system includes data stores and a campaign manager system. The campaign manager system will automatically allocate digital media assets to delivery slots in a campaign to generate scheduling files that media servers will use to present the allocated media assets to consumers during the assigned delivery slots via media consumption devices.

Aspects of the subject disclosure may include, for example, a device, that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, where the operations include detecting each individual of an audience viewing media content on user equipment; retrieving a user profile for each individual of the audience resulting in user profiles; creating a group profile from the user profiles; determining, based on the group profile, a recommendation for viewing a candidate media content; and providing the recommendation to the user equipment for the audience. Other embodiments are disclosed.

Embodiments are provided for receiving media content based on the user media preferences. An example implementation includes a one or more servers receiving data representing a guest list for an upcoming event corresponding to a first user account, the guest list indicating multiple guests corresponding to respective second user accounts of a second cloud service and querying one or more streaming media services for music preferences corresponding to the multiple guests. The one or more servers receive, from the one or more streaming media services, data representing respective music preferences corresponding to the multiple guests and generate a playlist of audio tracks based on the received respective music preferences corresponding to the multiple guests. During the event, the server(s) cause the playlist to be queued in a playback queue for playback by one or more playback devices of a particular media playback system registered with the first user account.

A search query can be received. User parameters can be identified based on the search query. The search query can be refined to include the user parameters. A search result from a search for media content using the refined search query can be received. Based on at least one search result received from the search and based on the user parameters, an augmented media content can be generated. Playing of the augmented media content can be synchronized with a user's activity by controlling playing of the augmented media content while detecting the user's activity pace.