Systems and methods for item recommendation are described. Embodiments of the present disclosure receive input indicating a relationship between a user and a first content item; generate a knowledge graph based on the input, wherein the knowledge graph comprises relationship information between the user and a plurality of content items; generate a first feature embedding representing the user and a second feature embedding representing a second content item of the plurality of content items based on the knowledge graph, wherein the second feature embedding is generated using a first modality for a query vector of an attention mechanism and a second modality for a key vector and a value vector of the attention mechanism; compare the first feature embedding to the second feature embedding to obtain a similarity score; and recommend the second content item for the user based on the similarity score.

Systems and methods for item recommendation are described. Embodiments of the present disclosure receive input indicating a relationship between a user and a first content item; generate a knowledge graph based on the input, wherein the knowledge graph comprises relationship information between the user and a plurality of content items; generate a first feature embedding representing the user and a second feature embedding representing a second content item of the plurality of content items based on the knowledge graph, wherein the second feature embedding is generated using a first modality for a query vector of an attention mechanism and a second modality for a key vector and a value vector of the attention mechanism; compare the first feature embedding to the second feature embedding to obtain a similarity score; and recommend the second content item for the user based on the similarity score.

Disclosed is a system for generating personalized recommendations based on dynamic and customized content selections and modeling of the content selections. The system may receive a request with an identifier and a query, and may obtain a particular recommendation configuration based the identifier and the query. The system may retrieve a set of content that satisfies the query and that is identified with at least one content prioritization parameter specified in the particular recommendation configuration, may generate a set of models of one or more model types that model relevance between the set of content and a different event specified in the particular recommendation configuration, and may compute a score for each content in each model based on the modeled relevance. The system may present recommended content in a different order than the set of content based on aggregate scores compiled for each content from the set of models.

Disclosed is a system for generating personalized recommendations based on dynamic and customized content selections and modeling of the content selections. The system may receive a request with an identifier and a query, and may obtain a particular recommendation configuration based the identifier and the query. The system may retrieve a set of content that satisfies the query and that is identified with at least one content prioritization parameter specified in the particular recommendation configuration, may generate a set of models of one or more model types that model relevance between the set of content and a different event specified in the particular recommendation configuration, and may compute a score for each content in each model based on the modeled relevance. The system may present recommended content in a different order than the set of content based on aggregate scores compiled for each content from the set of models.

Systems and methods are described herein for a media guidance application that detects, and responds to, a user's review of video content on a media device. The media guidance application detects a rewind operation during playback of a video comprising a media asset. In response, the media guidance application determines if the playback position reached during the rewind operation occurs during a first break in the media asset and, if so, identifies objects depicted in the video at the playback position, and presents auxiliary video relating to an object at a second break in the media asset.

Systems and methods are described herein for a media guidance application that detects, and responds to, a user's review of video content on a media device. The media guidance application detects a rewind operation during playback of a video comprising a media asset. In response, the media guidance application determines if the playback position reached during the rewind operation occurs during a first break in the media asset and, if so, identifies objects depicted in the video at the playback position, and presents auxiliary video relating to an object at a second break in the media asset.

Methods, systems, and media for demoting search results based on search query risk are provided. In some embodiments, a method for demoting search results includes: receiving a search query for a video content item; generating a plurality of search results in response to the search query; calculating a set of result goodness values; calculating a query goodness value associated with the search query based on the set of result goodness values; identifying a threshold goodness value based on the query goodness value; in response to determining that a first result goodness value of the set of result goodness values is less than the threshold goodness value, demoting a first search result of the plurality of search results, wherein the first result goodness value is associated with the first search result; and causing at least a portion of the plurality of search results to be presented based on the demotion.

Methods, systems, and media for demoting search results based on search query risk are provided. In some embodiments, a method for demoting search results includes: receiving a search query for a video content item; generating a plurality of search results in response to the search query; calculating a set of result goodness values; calculating a query goodness value associated with the search query based on the set of result goodness values; identifying a threshold goodness value based on the query goodness value; in response to determining that a first result goodness value of the set of result goodness values is less than the threshold goodness value, demoting a first search result of the plurality of search results, wherein the first result goodness value is associated with the first search result; and causing at least a portion of the plurality of search results to be presented based on the demotion.

A storage system and method for optimized surveillance search are provided. In one embodiment, a storage system is provided comprising a memory and a controller. The controller is configured to: receive, from a host, an image of an object and a logical block address range of video data stored in the memory; search for the image of the object in video data in the logical block address range; and provide the host with possible hits from the search. Other embodiments are provided.

A storage system and method for optimized surveillance search are provided. In one embodiment, a storage system is provided comprising a memory and a controller. The controller is configured to: receive, from a host, an image of an object and a logical block address range of video data stored in the memory; search for the image of the object in video data in the logical block address range; and provide the host with possible hits from the search. Other embodiments are provided.