Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.

Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method selects an entity from a plurality of entities identifying characteristics of a video item, where the video item has associated metadata. The computer-implemented method receives probabilities of existence of the entity in video frames of the video item, and selects a video frame determined to comprise the entity responsive to determining the video frame having a probability of existence of the entity greater than zero. The computer-implemented method determines a scaling factor for the probability of existence of the entity using the metadata of the video item, and determines an adjusted probability of existence of the entity by using the scaling factor to adjust the probability of existence of the entity. The computer-implemented method labels the video frame with the adjusted probability of existence.

Abstract: A system and methodology provide for annotating videos with entities and associated probabilities of existence of the entities within video frames. A computer-implemented method identifies an entity from a plurality of entities identifying characteristics of video items. The computer-implemented method selects a set of features correlated with the entity based on a value of a feature of a plurality of features, determines a classifier for the entity using the set of features, and determines an aggregation calibration function for the entity based on the set of features. The computer-implemented method selects a video frame from a video item, where the video frame having associated features, and determines a probability of existence of the entity based on the associated features using the classifier and the aggregation calibration function.

Abstract: A search result is modified to include a predetermined number of channels within a predetermined number of a plurality of entries in the search result. The search result is generated in response to a search query. The search result is modified to comprise a predetermined number of channels within a predetermined number of the plurality of entries. The modified search result is updated based on the plurality of entries and a subset of the updated modified search result is selected to be presented in response to the search query. The subset of the updated modified search result comprises the channel and at least one selection of data content.

Abstract: A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.

Abstract: Methods, systems, and media for ranking content items using topics are provided. In some embodiments, a method for ranking video content is provided, comprising: receiving a search query; generating a plurality of search results in response to the search query; determining one or more entity types associated with a content class within the plurality of search results; determining whether the search query is a query for content belonging to the content class based on a plurality of criterion that includes: (i) determining whether at least one of the plurality of search results is associated with the one or more determined entity types and (ii) determining whether entities shared between the plurality of search results include the one or more determined entity types; and in response to determining that the plurality of criterion have been met, promoting at least one search result of the plurality of search results belonging to the content class.

Abstract: The present disclosure relates to the identification of video content. In one aspect, a method includes generating a query based on bibliographic data. The method also includes obtaining a collection of resources responsive to the query, wherein one or more of the resources include text and video content. The method further includes calculating occurrence scores for the resources. A particular occurrence score for a particular resource is based at least in part on the bibliographic data matching text included in the particular resource and the text being associated with video content. The method further includes selecting one or more resources as including video content identified by the bibliographic data using the occurrence scores. The method further includes storing data associating the selected resources with the bibliographic data.

Abstract: Structured metadata extraction may include accessing one or more documents from which to extract the structured metadata from each of a plurality of hosts. A plurality of entity names can be extracted from the one or more documents from one of the plurality of hosts using an entity name pattern. A first element list can be extracted from the one or more documents based at least in part on the plurality of entity names and based at least in part on one or more heuristic rules. An element list pattern may be generated based at least in part on the first element list, and a second element list may be extracted from the one or more documents based at least in part on the element list pattern.

Abstract: A computer system determines feature metrics for a content channel. The content channel includes one or more media items. The computer system receives a query that corresponds to a query types. The query type is associated with a subset of the feature metrics. The computer system evaluates the content channel based on the subset of feature metrics to produce a channel score for the query type and provides the channel score to position at least one of the media items of the content channel in a query result of the query.

Abstract: An initial plurality of search results is obtained based on a search query pertaining to a topic of interest. A determination is made of whether to perform clustering on the initial plurality of search results. Upon determining not to perform clustering on the search result, a user interface with the initial plurality of search results is provided. Upon determining to perform clustering on the initial plurality of search results, a user interface with a modified plurality of search results is provided. The user interface is to position a first result in the modified plurality of search results adjacent to a position of a second search result in the modified plurality of search results. The first search result is associated with a channel and the second search result is associated with the channel.

Abstract: A search result is modified to include a predetermined number of channels within a predetermined number of a plurality of entries in the search result. The search result is generated in response to a search query. The search result is modified to comprise a predetermined number of channels within a predetermined number of the plurality of entries. The modified search result is updated based on the plurality of entries and a subset of the updated modified search result is selected to be presented in response to the search query. The subset of the updated modified search result comprises the channel and at least one selection of data content.

Abstract: Obtaining search results responsive to a query, each search result identifying a respective resource and each resource having a respective rank determined by a primary ranking algorithm. The method includes obtaining primary user feedback data and secondary user feedback data for the resource. The primary and secondary user feedback data representing previous user interactions with the resource when the resource was presented as a search result responsive to the query. The method also includes applying a weight to the secondary user feedback data, the weight being at least partially based on a threshold quantity of the primary user feedback data. The method also includes aggregating the primary user feedback data and the weighted secondary user feedback data and modifying the respective rank of the resource as a search result for the query based at least partially on the aggregated data.