Provided are a method for pushing anchor information, a computer device, and a storage medium. The method for pushing the anchor information includes: recalling an anchor user; determining an anchor type of the anchor user by using a live streaming history and a live streaming efficiency as classification dimensions; calculating an interaction score of the anchor user, wherein the interaction score represents a feature of a viewer user viewing a live streaming room of the anchor user; calculating a comprehensive score of the anchor user based on the interaction score and the anchor type; and pushing anchor information of the anchor user to the viewer user based on the comprehensive score.

Methods, systems, and machine-readable media are provided to facilitate content receiver control for particularized output of content items based on intra-content metrics. Observation data, corresponding to indications of detected content receiver operations associated with a content receiver and mapped to a first set of content items, may be processed. A first set of intra-content metrics may be detected. An audiovisual pattern of intra-content metrics may be mapped based on correlating the set of observation data with the first set of intra-content metrics. A second set of content items may be processed to detect a second set of intra-content metrics. A subset of the second set of content items may be selected based on a visual category and/or an audio category of the audiovisual pattern of intra-content metrics. The subset may be specified to cause a content receiver to modify operations to record and/or output content corresponding to the subset.

Mechanisms for selecting formats for streaming media content items are provided. In some embodiments, methods for selecting formats for streaming media content items are provided that include: receiving, at a server from a user device, a request to begin streaming a video content item on the user device; receiving, from the user device, network information indicating a quality of a network connection of the user device to a communication network used to stream the video content item and device information related to the user device; selecting, by the server, a first format for the video content item, wherein the first format includes a first resolution of a plurality of resolutions based on the network information and the device information; transmitting, from the server, a first portion of the video content item having the first format to the user device; receiving, at the server from the user device, updated network information and updated device information; selecting, by the server, a second format for the video content item, wherein the second format includes a second resolution of the plurality of resolutions based on the updated network information and the updated device information; and transmitting, from the server, a second portion of the video content item having the second format to the user device.

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.

Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.

A method of recommending media items to a user is provided. The method includes receiving historical data for a user of a media providing service. The historical data indicates past interactions of the user with media items. The method includes generating a model of the user. The model includes a first set of parameters, each of the first set of parameters quantifying a predicted latent preference of the user for a respective media item provided by the media providing service. The method includes evaluating the predicted latent preferences of the user for the respective media items against the historical data indicating the past interactions of the user with the media items provided by the media providing service. The method includes selecting a recommender system from a plurality of recommender systems using the model of the user, including the first set of parameters. The method includes providing a media item to a second user using the selected recommender system.

Aspects of the subject disclosure may include, for example, applying first data associated with a first content item to a model to generate first classification characteristics, analyzing the first classification characteristics to generate a first marker, wherein the first marker delineates a first location of inventory within the first content item, selecting a first creative to populate a portion of the inventory, and populating, based on the selecting, the portion of the inventory with the first creative. Other embodiments are disclosed.

Aspects of the subject disclosure may include, for example, applying first data associated with a first content item to a model to generate first classification characteristics, analyzing the first classification characteristics to generate a first marker, wherein the first marker delineates a first location of inventory within the first content item, selecting a first creative to populate a portion of the inventory, and populating, based on the selecting, the portion of the inventory with the first creative. Other embodiments are disclosed.

Systems, methods, and devices relating to determining an introduction portion in a video program are described herein. A method may determine first and second hard-matching pairs of video segments in first and second video content such that video fingerprints of the first hard-matching pair match and video fingerprints of the second hard-matching pair also match. The method may classify a third pair of video segments in the first and second video content, sequentially between the first and second hard-matching pairs, as a soft-matching pair of video segments of an introduction portion. The method may use the classification of the third pair of video segments as a soft-matching pair to determine a model configured to determine that a pair of video segments in two video content items are a soft-matching pair of video segments of an introduction portion.

Methods, systems, and computer-readable media for media classification using local and global audio features are disclosed. A media classification system determines local features of an audio input using an audio event detector model that is trained to detect a plurality of audio event classes descriptive of objectionable content. The local features are extracted using maximum values of audio event scores for individual audio event classes. The media classification system determines one or more global features of the audio input using the audio event detector model. The global feature(s) are extracted using averaging of clip-level descriptors of a plurality of clips of the audio input. The media classification system determines a content-based rating for media comprising the audio input based (at least in part) on the local features of the audio input and based (at least in part) on the global feature(s) of the audio input.