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 used for generating content-item recommendations. 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, as well as modifying this score data using one or more biasing factors for generating result data. In addition, the techniques, devices, and systems may use this result data, along with received user input, for determining an order in which to present one or more content items to the user. For example, this may include determining which content items to recommend to a user and in which order to do so.

Abstract: Embodiments of systems and methods for automated real-time routing within a fleet of geographically distributed drivers are disclosed. Embodiments may operate to dispatch orders and determine routing in real-time in a geographic area through application of rule-based filtering of drivers and selective application of optimal or non-optimal routing solutions utilizing the real-time locations of drivers, real-time conditions within the geographic area and the locations for the set of orders being routed by the system.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models used for generating content-item recommendations. 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, as well as modifying this score data using one or more biasing factors for generating result data. In addition, the techniques, devices, and systems may use this result data, along with received user input, for determining an order in which to present one or more content items to the user. For example, this may include determining which content items to recommend to a user and in which order to do so.

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: Embodiments of systems and methods for automated real-time routing within a fleet of geographically distributed drivers are disclosed. Embodiments may operate to dispatch orders and determine routing in real-time in a geographic area through application of rule-based filtering of drivers and selective application of optimal or non-optimal routing solutions utilizing the real-time locations of drivers, real-time conditions within the geographic area and the locations for the set of orders being routed by the system.

Abstract: Embodiments of systems and methods for automated real-time routing within a fleet of geographically distributed drivers are disclosed. Embodiments may operate to dispatch orders and determine routing in real-time in a geographic area through application of rule-based filtering of drivers and selective application of optimal or non-optimal routing solutions utilizing the real-time locations of drivers, real-time conditions within the geographic area and the locations for the set of orders being routed by the system.

Abstract: Described herein are, among other things, techniques, devices, and systems for generating one or more trained machine-learning models used for generating content-item recommendations. 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, as well as modifying this score data using one or more biasing factors for generating result data. In addition, the techniques, devices, and systems may use this result data, along with received user input, for determining an order in which to present one or more content items to the user. For example, this may include determining which content items to recommend to a user and in which order to do so.

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: Embodiments of systems and methods for automated real-time routing within a fleet of geographically distributed drivers are disclosed. Embodiments may operate to dispatch orders and determine routing in real-time in a geographic area through application of rule-based filtering of drivers and selective application of optimal or non-optimal routing solutions utilizing the real-time locations of drivers, real-time conditions within the geographic area and the locations for the set of orders being routed by the system.