Abstract: In some embodiments, a method maps attributes of metadata for a plurality of content instances to metadata nodes. The metadata nodes are connected to type nodes that define a type of metadata for each metadata node and a respective content node for a respective content instance. The method generates a plurality of sample paths using the content nodes, the metadata nodes, and the type nodes from the mapping of attributes of the metadata. A similarity of content nodes is analyzed using the plurality of sample paths. Then, the method generates a representation of each of the plurality of content instances using the similarity of the content nodes. The representation represents the similarity between content instances in the plurality of content instances.

Abstract: In some embodiments, a method maps attributes of metadata for a plurality of content instances to metadata nodes. The metadata nodes are connected to type nodes that define a type of metadata for each metadata node and a respective content node for a respective content instance. The method generates a plurality of sample paths using the content nodes, the metadata nodes, and the type nodes from the mapping of attributes of the metadata. A similarity of content nodes is analyzed using the plurality of sample paths. Then, the method generates a representation of each of the plurality of content instances using the similarity of the content nodes. The representation represents the similarity between content instances in the plurality of content instances.

Abstract: In some embodiments, a method trains a first prediction network to predict similarity between images in videos. The training uses boundaries detected in the videos to train the prediction network to predict images in a same scene to have similar feature descriptors. The first prediction network generates feature descriptors that describe library images from videos in a video library offered to users of a video delivery service. A search image is received and the prediction network predicts one or more library images for one or more videos that are predicted to be similar to the received image. The one or more library images for the one or more videos are provided as a search result.

Abstract: In some embodiments, a method trains a first prediction network to predict similarity between images in videos. The training uses boundaries detected in the videos to train the prediction network to predict images in a same scene to have similar feature descriptors. The first prediction network generates feature descriptors that describe library images from videos in a video library offered to users of a video delivery service. A search image is received and the prediction network predicts one or more library images for one or more videos that are predicted to be similar to the received image. The one or more library images for the one or more videos are provided as a search result.