Abstract: A computer-implemented method includes determining a set of target listings, retrieving a seed image associated with the seed listing, the seed listing is categorized within a first item category, and generating a seed item feature vector for the seed image using a convolutional neural network (CNN) trained with images of items. The method also includes identifying a plurality of feature vectors associated with the first item category, comparing the seed item feature vector to the plurality of feature vectors using a k-nearest neighbors (kNN) algorithm, and generating a set of nearest neighbor listings to the seed listing. The method further includes storing the set of nearest neighbor listings as associated with the seed listing, selecting one or more nearest neighbor listings from the set of nearest neighbors, and presenting the one or more nearest neighbor listings as a recommendation to a user of the online e-commerce system.

Abstract: In various embodiments, methods and systems for implementing an integrated image search system are provided. The integrated image search system supports an image search feature of a content application on a mobile device. An image identifier is received to execute an image search operation. The image identifier is received via a native operating system action that is customized to support the image search feature. The native operating system action defines an entry point to the image search feature. The entry points can be based on image identifiers identified from a share image action, a share Uniform Resource Locator (URL) action, a copy share URL action, or a drag and drop action. An image of the image identifier is used to execute the image search operation to identify image search results. An image search results page having one or more images search results is caused to be displayed.

Abstract: A computer-implemented method includes determining a set of target listings, retrieving a seed image associated with the seed listing, the seed listing is categorized within a first item category, and generating a seed item feature vector for the seed image using a convolutional neural network (CNN) trained with images of items. The method also includes identifying a plurality of feature vectors associated with the first item category, comparing the seed item feature vector to the plurality of feature vectors using a k-nearest neighbors (kNN) algorithm, and generating a set of nearest neighbor listings to the seed listing. The method further includes storing the set of nearest neighbor listings as associated with the seed listing, selecting one or more nearest neighbor listings from the set of nearest neighbors, and presenting the one or more nearest neighbor listings as a recommendation to a user of the online e-commerce system.

Abstract: In various embodiments, methods and systems for implementing an integrated image search system are provided. The integrated image search system supports an image search feature of a content application on a mobile device. An image identifier is received to execute an image search operation. The image identifier is received via a native operating system action that is customized to support the image search feature. The native operating system action defines an entry point to the image search feature. The entry points can be based on image identifiers identified from a share image action, a share Uniform Resource Locator (URL) action, a copy share URL action, or a drag and drop action. An image of the image identifier is used to execute the image search operation to identify image search results. An image search results page having one or more images search results is caused to be displayed.

Abstract: A media analysis system includes one or more hardware processors, a memory storing synopses associated with media items, and a content analysis engine. The content analysis engine generates a media vector for each media item based on the associated synopsis by generating a word vector for each word in the synopsis, combining the plurality of word vectors into a mean vector for the media item, and storing the mean vector as the media vector associated with the media item. The content analysis engine also identifies a target media item associated with a seed media vector, determines R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items, clusters the R nearest neighbors into K clusters, and selects media items for recommendation to a user based on the K clusters.

Abstract: A media analysis system includes one or more hardware processors, a memory storing synopses associated with catalog books, and a content analysis engine. The content analysis engine generates a media vector for each catalog book based on the associated synopsis by generating a word vector for each word in the synopsis, combining the plurality of word vectors into a mean vector for the catalog book, and storing the mean vector as the media vector associated with the catalog book. The content analysis engine also identifies a target book associated with a seed media vector, determines R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books, clusters the R nearest neighbors into K clusters, and selects catalog books for recommendation to a user based on the K clusters.

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media directed to generating interaction and network asset association predictions. Recommendations for network assets in response to interactions with other network assets may be provided. Initially, a pair of items, including a seed asset and a candidate asset, is received. Each word in the seed and candidate titles, each aspect, and the categories may be embedded into a k-dimensional vector space. The embedding may then be aggregated to construct an n-dimensional vector representing a seed asset and an n-dimensional vector representing a candidate asset which are used to determine and generate a probability that the seed asset and the candidate asset are contemporaneously operated upon by the same user. The system may then rank recommendation candidates by a co-interaction probability output of the neural network system.

Abstract: A computer-implemented method includes determining a set of target listings, retrieving a seed image associated with the seed listing, the seed listing is categorized within a first item category, and generating a seed item feature vector for the seed image using a convolutional neural network (CNN) trained with images of items. The method also includes identifying a plurality of feature vectors associated with the first item category, comparing the seed item feature vector to the plurality of feature vectors using a k-nearest neighbors (kNN) algorithm, and generating a set of nearest neighbor listings to the seed listing. The method further includes storing the set of nearest neighbor listings as associated with the seed listing, selecting one or more nearest neighbor listings from the set of nearest neighbors, and presenting the one or more nearest neighbor listings as a recommendation to a user of the online e-commerce system.

Abstract: A media analysis system includes one or more hardware processors, a memory storing synopses associated with catalog books, and a content analysis engine. The content analysis engine generates a media vector for each catalog book based on the associated synopsis by generating a word vector for each word in the synopsis, combining the plurality of word vectors into a mean vector for the catalog book, and storing the mean vector as the media vector associated with the catalog book. The content analysis engine also identifies a target book associated with a seed media vector, determines R nearest neighbors for the target book from the plurality of catalog books based on (1) the seed media vector and (2) the media vectors associated with the plurality of catalog books, clusters the R nearest neighbors into K clusters, and selects catalog books for recommendation to a user based on the K clusters.

Abstract: Pose and gesture detection and classification of a human poses and gestures using a discriminative ferns ensemble classifier is provided. Sample image data in one or more channels includes a human image. A processing device operates on the sample image data using the discriminative ferns ensemble classifier. The classifier has set of classification tables and matching bit features (ferns) which are developed using a first set of training data and optimized by a weighting of the tables using an SVM linear classifier configured based on the first or a second set of pose training data. The tables allow computation of a score per pose class for the image in the sample data and the processor outputs a determination of the pose in the sample depth image data. The determination enables the manipulation of a natural user interface.