Methods, systems, and computer programs for generating a playlist of media content items for a group of users. Media content items listened to by the selected users are compared to an average user taste profile to select media content items for playback to the group of users.

Systems and methods for enhancing virality for a content item are disclosed herein. A content item is uploaded to a content sharing platform over a communication network. Feedback on the content item is received from the content sharing platform over the communication network. Based on the feedback, a virality score for the content item is determined and a determination is made as to whether the virality score meets a virality criterion. In response to a determination that the virality score does not meet the virality criterion, a virality enhancement technique is selected from a virality enhancement database, the content item is modified by applying the virality enhancement technique to the content item, and the modified content item is uploaded to the content sharing platform over the communication network.

This document describes, among other things, systems, methods, devices, and other techniques for using information about how long various videos were presented at client devices to determine subsequent video recommendations and search results. In some implementations, a computing can include a modeling apparatus, a front-end server, a request manager, one or more video file storage devices, a video selector, or a combination of some or all of these. The video selector can select video content for a particular digitized video among a plurality of digitized videos to serve to a computing device responsive to a request. The selection can be based at least in part on how long the particular digitized video has been presented at client devices associated with users having characteristics that match one or more characteristics of the user that submitted the request for video content, as indicated by the modeling apparatus.

A system and method are disclosed for training a machine learning model using information pertaining to transmissions of one or more media items to user devices associated with a user account. Generating training data for the machine learning model includes generating first contextual information associated with a first user device and generating a first target output that identifies an indication of a preference of a user preference to cancel the first transmission. The method includes providing the training data to train the machine learning model.

Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to perform probabilistic modeling for anonymized data integration and measurement of sparse and weakly-labeled datasets are disclosed. An apparatus includes a training controller to train a neural network to produce a trained neural network to output model parameters of a probability model, a model evaluator to execute the trained neural network on input data specifying a time of day, a media source, and at least one feature different from the time of day and the media source to determine one or more first model parameters of the probability model, and a ratings metric generator to evaluate the probability model based on input census data to determine a ratings metric corresponding to the time of day, the media source, and the at least one feature, the probability model configured with the one or more first model parameters.

Methods, apparatus and systems related to production of a movie, a TV show or a multimedia content are described. In one example aspect, a system for producing a multimedia digital content includes a pre-production subsystem configured to receive information about a storyline, cameras, cast, and other assets for the content from a user. The pre-production subsystem is configured to generate one or more machine-readable scripts that include information about one or more advertisements. The system includes a production subsystem configured to receive the one or more machine-readable scripts from the pre-production system to obtain a footage according to the storyline. The production subsystem is further configured to embed one or markers corresponding to the one or more advertisements in the footage. The system also includes a post-production editing subsystem configured to detect the one or more markers embedded in the footage and replace each of the one or more markers with a corresponding advertising target.

Methods and apparatus to detect spillover are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to: identify a quantity of first durations of loudness in an audio signal of media; calculate a ratio of the quantity of the first durations of loudness to a quantity of second durations of loudness in the audio signal of the media, the quantity of the second durations of loudness including the quantity of the first durations of loudness; and in response to a detection of the audio signal being spillover, store data denoting the media as un-usable to credit a media exposure when the ratio does not satisfy a loudness ratio threshold, the storing of the data to improve an accuracy of media exposure credits by not crediting spillover media.

A request from a user to view a video content item may be received, the requesting user being associated with a set of preferences and a context. A group of similar users may be identified based the set of preferences or the context. A number of promotional video items corresponding to the video content item may be identified. A first subset of the number of promotional video items may be determined based on the set of preferences or the context of the user. A watch time difference may be determined for each promotional video item in the first subset. A second subset may be determined based on the watch time difference associated with each promotional video items. An activity rate associated with the promotional video items in the second subset is determined. A promotional video item of the second subset that satisfies a criterion is provided to the user.

Systems and methods for providing synchronous transmission of streaming media are disclosed. One method may include: receiving, from a first user device associated with a first user, a request to invite a second user to a virtual media streaming session; retrieving, from the at least one database, a second user profile, the second user profile identifying a second user device associated with the second user; transmitting, subsequent to the retrieving, instructions to the second user device to present a notification alerting the second user of the request; determining, using a processor, whether a response accepting the request is detected from the second user device; and connecting, responsive to determining that the response accepting the request was detected, the second user profile to the virtual media streaming session; wherein multimedia content presented in the virtual media streaming session is simultaneously viewable on the first user device and the second user device.

A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.