Abstract: Techniques image suitability determination to generate augmented reality/virtual reality (AR/VR) digital content are described. A two-dimensional digital image is received. Using machine-learning, a determination as to whether an object captured by the two-dimensional digital image is suitable for generating AR/VR digital content for display in an AR/VR environment is made. If the object is suitable, an indication is provided and an option to view the object in an AR/VR environment is provided. If the object is not suitable, a suggestion indicating why the object as captured is not suitable and/or how to correct the capture of the object in a subsequent digital image such that it is suitable for generating AR/VR digital content.

Abstract: Techniques image suitability determination to generate augmented reality/virtual reality (AR/VR) digital content are described. A two-dimensional digital image is received. Using machine-learning, a determination as to whether an object captured by the two-dimensional digital image is suitable for generating AR/VR digital content for display in an AR/VR environment is made. If the object is suitable, an indication is provided and an option to view the object in an AR/VR environment is provided. If the object is not suitable, a suggestion indicating why the object as captured is not suitable and/or how to correct the capture of the object in a subsequent digital image such that it is suitable for generating AR/VR digital content.

Abstract: Techniques image suitability determination to generate augmented reality/virtual reality (AR/VR) digital content are described. A two-dimensional digital image is received. Using machine-learning, a determination as to whether an object captured by the two-dimensional digital image is suitable for generating AR/VR digital content for display in an AR/VR environment is made. If the object is suitable, an indication is provided and an option to view the object in an AR/VR environment is provided. If the object is not suitable, a suggestion indicating why the object as captured is not suitable and/or how to correct the capture of the object in a subsequent digital image such that it is suitable for generating AR/VR digital content.

Abstract: Techniques image suitability determination to generate augmented reality/virtual reality (AR/VR) digital content are described. A two-dimensional digital image is received. Using machine-learning, a determination as to whether an object captured by the two-dimensional digital image is suitable for generating AR/VR digital content for display in an AR/VR environment is made. If the object is suitable, an indication is provided and an option to view the object in an AR/VR environment is provided. If the object is not suitable, a suggestion indicating why the object as captured is not suitable and/or how to correct the capture of the object in a subsequent digital image such that it is suitable for generating AR/VR digital content.

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: A system and method to evaluate the affinity of a collection of sale items to a user's interests. The affinity is a measure of how closely a user's interests match the contents of a collection (e.g., a collection of items selected by a seller, other user, or employee of the sales site). The method may determine the affinity of various collections by using a vector-space distance measure between the user's categories of interest and the relative percentages of various categories of items in each collection's. The method may also add a quality score for the collection to the affinity score and/or a random value to ensure that the system recommends high quality collections does not recommend the same set of collections every time the user logs in or visits the sales site.

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.