A content segmentation system includes a computing platform having processing hardware and a system memory storing a software code and a trained machine learning model. The processing hardware is configured to execute the software code to receive content, the content including multiple sections each having multiple content blocks in sequence, to select one of the sections for segmentation, and to identify, for each of the content blocks of the selected section, at least one respective representative unit of content. The software code is further executed to generate, using the at least one respective representative unit of content, a respective embedding vector for each of the content blocks of the selected section to provide a multiple embedding vectors, and to predict, using the trained machine learning model and the embedding vectors, subsections of the selected section, at least some of the subsections including more than one of the content blocks.

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.

Systems and methods are presented herein for recording portions of a media asset relevant to recording criteria. A media application receives input indicating the recording criteria and identifying a first keyword. The media application accesses a data structure to identify a first node associated with the first keyword. The data structure includes the first node and a plurality of nodes connected to the first node via a plurality of paths. The media application receiving audio component data for a portion of the media asset extracts a term from the audio component data, and identifies a second node in the data structure that is associated with the extracted term. The media application calculates a path score for the portion of the media asset based on a path size in the data structure between the first node and the second node. When the score is high enough, the portion of the media asset is recorded.

Methods, systems, and non-transitory, machine-readable media are disclosed for audio adaption of content items to device operations of an endpoint media device. First observation data corresponding to media device operations associated with a first media device and mapped to first content items may be processed. A first content composite including an adaptable content item may be received. The first content composite may be adapted with a first audio segment. Based on the first observation data, the first audio segment may be selected. The first content composite may be configured with the first audio segment so that the adapted first content composite plays the first audio segment when the adapted first content composite is presented. The adapted first content composite may be output for presentation, where the first endpoint media device or the second endpoint media device performs at least one operation relating to the adapted first content composite.

An input of a constraint parameter associated with a constraint required when optimizing a target is received. An objective function to be utilized for the optimization of the target is computed by using optimized results by an expert who has performed the optimization in the past, the constraint parameter, and an inverse optimization technique. The target is optimized based on the objective function.

A streaming system includes a streaming server and a client device. The streaming server is configured to train an interactive frame prediction model based on streaming data, a user input and metadata associated with the user input, encode the streaming data by selectively using a predicted frame generated based on the trained interactive frame prediction model and transmit the trained interactive frame prediction model and the encoded streaming data. The client device is configured to receive the trained interactive frame prediction model and the encoded streaming data, and decode the encoded streaming data based on the trained interactive frame prediction model to provide recovered streaming data to a user.

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.

Methods, systems, and media for selecting video formats for adaptive video streaming are provided. In some embodiments, the method comprises: receiving an indication of a video to be presented on a user device; identifying a group of quality metrics for each of a plurality of segments of the video, wherein each quality metric includes values for a particular segment and for a particular format of a group of available formats for the video; selecting a first format for a first segment of the video; causing the first segment of the video to be presented on the user device; identifying a quality of a network connection between the user device and a server that hosts the video; identifying a second format for a second segment of the video based on the quality of the network connection; determining whether a format of the video is to be changed from the first format to the second format based at least on the group of quality metrics for the second segment of the video; and, in response to determining that the format of the video is to be changed from the first format to the second format, causing the second segment having the second format to be presented by the user device.

An example method includes training a machine learning algorithm to detect a granular level of tags associated with a media content, receiving a request to play a user selected media content, applying the machine learning algorithm that is trained to the user selected media content to identify at least one tag of the granular level of tags associated with the user selected media content, generating a notification that reports the at least one tag and a number of occurrences of the at least one tag in the user selected media content, and transmitting the notification to an endpoint device of a user that sent the request for the user selected media content to display the notification.