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 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 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: Systems and methods for generating prompts for further data from a user in a multi-turn interactive dialog. Embodiments improve searches for the most relevant items available for purchase in an electronic marketplace via a processed sequence of user inputs and machine-generated prompts. Question type prompts, validating statement type prompts, and recommendation type prompts may be selectively generated based on whether a user query has been sufficiently specified, user intent is ambiguous, or a search mission has changed. Detection of a new dominant object denotes search mission change. Contextual associations between prompts and user replies are maintained, but a search mission change results in previous context data being disregarded.

Abstract: Systems and methods for selecting types of generated prompts for further data from a user in a multi-turn interactive dialog. In one scenario, a processed sequence of user inputs and machine-generated prompts improves searches for the most relevant items available for purchase in an electronic marketplace. The number of prompts may be limited to a predetermined maximum value. Prompt generation is minimized by incorporating into a knowledge graph world knowledge that helps user intent inference. Prompt generation may be suppressed if a search indicates the reply to a prompt will not lead to any satisfactory search results. Prompts can provide suggestions for available search results that either meet all query constraints, or meet only some query constraints if a search indicates no search results are available that meet all query constraints. Prompts can provide suggested incisive reply phrasing likely to improve search results through an affirmation or negation reply.

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.