Aspects of the subject disclosure may include, for example, a method, including identifying recommended video clips for a user of a communication device according to a video viewing profile for the user and video subject matter information associated with a plurality of video clips, identifying an edge cloud server for facilitating network access by the communication device at a location, determining a reduced activity period for a data path between a video content server and the edge cloud server by comparing activity information for the data path and an activity threshold, directing the video content server to store the recommended video clips at the edge cloud server during the reduced activity period, and providing a listing of the recommended video clips to an application, where the communication device receives a video clip of the recommended video clips from the edge cloud server responsive to a selection of the video clip via the application. Other embodiments are disclosed.

In some embodiments, a method receives an supplemental content placement and a context associated with a request for supplemental content to be displayed for the supplemental content placement. A first value is generated based on the context using a prediction network for a platform. The method determines probabilities for a plurality of types of request actions based on the context. Then, a threshold for the supplemental content placement is calculated based on the first value and the probabilities for the plurality of types of request actions. The method submits the threshold to a platform in a request for the platform to submit a second value for the supplemental content placement.

The present disclosure relates to a computer-implemented process for generating and providing simulated user absorption information pertaining to users and based on target profiles and target situations, thereby providing user targeted and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user profile targeted content recommendations for users of an interactive electronic system.

In one embodiment, an image analysis is performed on an image captured using a camera mounted on an autonomous vehicle, the image representing an exterior environment of an autonomous vehicle. Localization information surrounding the autonomous vehicle is obtained at a point in time. A perception of an audience external to the autonomous vehicle is determined based on the image analysis and the localization information. One or more content items are received from one or more content servers over a network in response to the perception of the audience. A first content item selected from the one or more content items is displayed on a display device mounted on an exterior surface of the autonomous vehicle.

A video recommending method, including: obtaining videos, the video including long videos and short videos; obtaining a subset of the long videos, of which video attribute values are greater than corresponding attribute thresholds; obtaining a watching record of a user, and obtaining similarities between the short videos and videos in the watching record, to extract a preset quantity of short videos having highest similarities; and recommending the subset of the long videos, of which video attribute values are greater than corresponding attribute thresholds, to the user, and/or recommending the preset quantity of short videos having highest similarities to the user.

The present disclosure provides a system and method for recommending dynamic, adaptive and non-sequentially assembled videos. The method includes reception of a set of preference data and a set of user authentication data. The method includes development of an interest profile of the user. The method includes fetching of the one or more tagged videos. The method includes fragmentation of each tagged video into the one or more tagged fragments and segregation of one or more mapped fragments into one or more logical sets of mapped fragments. The method includes mining of semantic context information from each mapped fragment and each logical set of mapped fragments. The method includes clustering of the one or more logical sets of mapped fragments and assembling of the one or more logical clusters of mapped fragments to obtain a set of assembled videos. The method includes recommendation of the set of assembled videos.

The present disclosure provides a system and method for enabling display of interactive questions during streaming of a real time, dynamic, adaptive and non-sequentially assembled video. The method includes reception of a set of preference data associated with a user and a set of user authentication data. In addition, the method includes serving of the assembled video based on the received set of preference data to the user in real time. Moreover, the method includes fetching of a pre-defined set of interactive questions. Further, the method includes posting of the pre-defined set of interactive questions during the serving of the assembled video. Furthermore, the method includes collection of the set of user feedbacks for a posted pre-defined set of interactive questions from the user.

The present disclosure provides a method and system for real time, dynamic, adaptive and non-sequential assembling of one or more mapped fragments of one or more tagged videos. The method includes a step of receiving a set of preference data from pre-defined selection criteria and set of user authentication data. The method includes another step of fetching the one or more tagged videos from the digitally processed repository of videos. The method includes yet another step of fragmenting each tagged video of the one or more tagged videos into the one or more tagged fragments and clustering one or more logical sets of mapped fragments into one or more logical clusters of mapped fragments. The method includes yet another step of assembling at least one of the one or more logical clusters of mapped fragments in a pre-defined order of preference to obtain an assembled video.

There are disclosed a method of and a system for selecting a network resource as a source of a content item, the content item to be analyzed by a recommendation system as part of a plurality of content items to generate a set of recommended content items as a recommendation for a given user of the recommendation system. The method comprises, for a network resource, receiving, by the server, a plurality of features associated with a network resource to be processed; generating given network resource profile for the network resource, the given network resource profile being based on the plurality of features; executing a machine learning algorithm in order to determine a source suitability parameter for the network resource, selecting at least one content item from the network resource if the source suitability parameter is determined to be above a pre-determined threshold.

In various embodiments, an optimization engine regenerates items included in an interactive page while the user is interacting with the interactive page. In operation, an optimization engine displays a portion of the interactive page during a viewing session. Subsequently, the optimization engine computes a probability distribution for the viewing session over a set of interests based on model parameters and operations performed by the user during the viewing session. The optimization engine then regenerates items that are included in a second portion of the interactive page based on the probability distribution for the viewing session. The optimization engine displays a least a part of the resulting regenerated interactive page. Advantageously, by regenerating items included in the interactive page based on operations performed by the user during the viewing session, the optimization engine reduces the time required for the user to view an item that piques an interest.