Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history. The techniques then determine a ranked list of content items having a highest correlation to the consumption history, which may be used to retrieve videos associated with the most-correlated content items for generating a compilation video composed of these retrieved videos.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history. The techniques then determine a ranked list of content items having a highest correlation to the consumption history, which may be used to retrieve videos associated with the most-correlated content items for generating a compilation video composed of these retrieved videos.

Abstract: The application is directed to techniques, devices, and systems for updating files. For example, a remote system may store first data representing a first version of a file and second data representing a second version of the file. The remote system may then determine that a first portion of the first data corresponds to a first portion of the second data. Based at least in part on the determination, the remote system may identify a second portion of the first data using the first portion of the first data and identify a second portion of the second data using the first portion of the second data. The remote system may then generate third data representing a difference between the second portion of the second data and the second portion of the first data. Additionally, the remote system may store the third data in association with the file.

Abstract: The application is directed to techniques, devices, and systems for updating files. For example, a remote system may store first data representing a first version of a file and second data representing a second version of the file. The remote system may then determine that a first portion of the first data corresponds to a first portion of the second data. Based at least in part on the determination, the remote system may identify a second portion of the first data using the first portion of the first data and identify a second portion of the second data using the first portion of the second data. The remote system may then generate third data representing a difference between the second portion of the second data and the second portion of the first data. Additionally, the remote system may store the third data in association with the file.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models. Also described herein are techniques, devices, and systems for applying a consumption history of a particular user to the trained model(s) to generate score data indicating a correlation between each content-item title and the consumption history. The techniques then determine a ranked list of content items having a highest correlation to the consumption history, which may be used to retrieve videos associated with the most-correlated content items for generating a compilation video composed of these retrieved videos.

Abstract: The application is directed to techniques, devices, and systems for updating files. For example, a remote system may store first data representing a first version of a file and second data representing a second version of the file. The remote system may then determine that a first portion of the first data corresponds to a first portion of the second data. Based at least in part on the determination, the remote system may identify a second portion of the first data using the first portion of the first data and identify a second portion of the second data using the first portion of the second data. The remote system may then generate third data representing a difference between the second portion of the second data and the second portion of the first data. Additionally, the remote system may store the third data in association with the file.