Abstract: Systems, devices, and methods are provided for determining contextually relevant user-based product recommendations based on scene information. In at least one embodiment, techniques described herein may be used to determine, using a first machine-learning model, first information associated with a first object within a first image of digital content, determine, using a second machine-learning model, similarity scores between the first object and a first plurality of products of an online purchasing system, detect, in association with the first image of the digital content, performance of a first computer-based action by a user, determine, using a third machine-learning model and based on contextual data of the user, one or more affinity scores for the user, select a first product based on the one or more affinity scores, and present a recommendation to the user to perform a second computer-based action in association with the first product.

Abstract: Some implementations include methods for generating a portable entitlement for a digital asset and may include generating a portable entitlement to a digital asset based on a request initiated by a first user having an entitlement to the digital asset, the portable entitlement to enable the first user to access the digital asset using a second computing device of a second user, the request initiated using a first computing device of the first user, the portable entitlement having a limited lifetime; and terminating the second computing device from accessing the digital asset based on one or more of determining that a proximity between the first and second computing devices violate the distance threshold and the lifetime of the portable entitlement has expired.

Abstract: Techniques are described for automating virtual placements in video content.

Abstract: Techniques are described for automating virtual placements in video content.