An online system presents content in videos to users. Content providers may value having their content injected into videos from certain sources more than others. This preferences is quantified as a brand value score. The brand value score is determined as a function of user engagement with a source of the video and, to account for brand value, the system performs a two-stage auction. First, the system determines whether to inject any content into a video by determining a distribution of brand value of videos per demand for videos in a previous period and filling a projected demand for the content in a current period to determine a brand value threshold. Then, any videos having a brand value above the threshold are eligible for the second stage of the selection process where the system performs an auction where projected benefit of presenting the content is compared to projected loss.

Systems, methods, and non-transitory computer-readable media can collect a set of training videos as training data, wherein the set of training videos are labeled with one or more labels based on one or more video quality metrics associated with an evaluation objective. A machine learning model is trained based on the training data. A video to be evaluated is received. The video is assigned to a first video quality category of a plurality of video quality categories based on the machine learning model.

Methods, apparatus, systems, and articles of manufacture are disclosed to monitor on-demand media. An example apparatus populates a reference library with a reference profile for on-demand media in response to publication of the on-demand media by generating one or more reference signatures for the on-demand media, collecting reference metadata associated with the on-demand media, and associating the one or more reference signatures and the reference metadata in the reference library to generate the reference profile. Additionally, the example apparatus collects one or more sample signatures generated at a media presentation environment. The example apparatus also records an exposure for the on-demand media based on a match between the one or more sample signatures and the one or more reference signatures.

An electronic device generates a respective user queue for each user of a plurality of users participating in a shared listening session. While providing a first media content item for playback, the device receives a second request, from a first user, to add a second media content item to the shared playback queue and updates the respective user queue for the first user. After receiving the second request, the electronic device receives a third request, from a second user, to add a third media content item to the shared playback queue and updates the respective user queue for the second user. The electronic device updates the shared playback queue using the respective user queues of the first user and the second user, including positioning the third media content item in an order of the shared playback queue to be played back before the second media content item.

Aspects of the subject disclosure may include, for example, a system and method for selecting media content for a group of persons located at a venue. The system and method identify a plurality of viewers in a vicinity of a venue that has one or more display devices from location data and extracts a content viewing preference of each viewer from their profile data. The process includes aggregating the content viewing preference of each of the plurality of viewers to generate an aggregated content profile. Based on the aggregated content profile, a list of content is generated. Next, a first group of viewers approaching a viewing range of a display device are sensed by imaging data. Any conflicts between content viewing preferences of the first group of viewers are detected and resolved based on weighting the viewing preferences of the group. Other embodiments are disclosed.

A content compilation system includes a computing platform having a hardware processor and a memory storing a software code configured to provide an editorial interface. The hardware processor executes the software code to receive compilation authoring data via the editorial interface, identify one or more end-user(s) for receiving a content compilation, access a consumption profile of the end-user(s), obtain, using the consumption profile and a first authoring criterion in the compilation authoring data, content items from one or more content sources. The software code further aggregates, using a second authoring criterion in the compilation authoring data, the content items into content subsets, groups, using a third authoring criterion, at least some of the content subsets to produce the content compilation, computes a desirability score predicting the desirability of the content compilation to the end-user(s), and provides, when the desirability score satisfies a predetermined threshold, the content compilation to the end-user(s).

Media content search results ranked by popularity is described. In embodiment(s), a search request for television media content can be initiated by a viewer, and television media content that is relevant to the search request can be identified. The relevant television media content can then be ranked based on a popularity rating and the relevant television media content can be displayed in an ordered list that is ordered by popularity rankings.

Social media content items are mapped to relevant time-based media events. These mappings are used as the basis for calculating metrics based upon the mappings, and ratings of the time-based media there from. Audience response metrics (ARMs) are calculated from the mappings, which provide an indication of audience engagement or response. In addition, ARMs provide information about the virality, depth, and breadth of the response by the viewing audience. The calculated metrics are stored and are used to generate ratings for the time-based media. The ratings may be published in whole or in part, e.g., as part of a ratings system.

Methods, systems, and media for indicating viewership of a video are provided. In some embodiments, the method comprises: identifying a video; identifying a first group of users; determining an affinity score for each user with the identified video; receiving a request to present a page that includes a representation of the video; identifying a second group of users connected to the first user associated with the user device; determining a viewership status of each user in the second group of users corresponding to the video; identifying a subset of users in the second group of users based at least in part on the viewership status; and causing groups of indicators to be presented on the user device, wherein each indicator in the groups of indicators represents the viewership status of the user, and wherein the indicators are presented on the requested page in connection with the representation of the video.

The current invention relates to a computer-generated method for optimizing placement of advertising content across multiple different devices. The system can allocate advertising campaigns and plans to various inventory types based on the probability of accurate consumer matching. Consumer matching can be achieved by generation of look-alike models in a consumer's device graph to predict future consumption behavior. The system includes an interface through which an advertiser can access relevant information about inventory and success of a given placement.