Abstract: Systems and methods are provided for receiving a plurality of images corresponding to a listing in an online marketplace, generating a scene type for each image of the plurality of images, and grouping each image into a scene type group of a set of predefined scene types. Each group of images are input into a respective machine learning model specific to the scene type of the group of images to generate a visual score for each image in each group of images, and an attractiveness score is generated for the listing in the online marketplace based on the visual scores for each image in each group of images.

Abstract: Systems and methods are provided for generating a base visual score for each candidate image of a plurality of images received by a computing system, based on the scene type of each image. For each candidate image, the computing system multiplies the base visual score by a feature importance weight to generate a first visual score, adds respective scene type bonus points to the first visual score to generate a second visual score, and adds diversity scoring points to the second visual score to generate a final visual score for each candidate image. The computing system ranks the candidate images based on the final visual scores and provides a specified number of the top-ranked candidate images to be displayed on a display of the computing device.

Abstract: Systems and methods are provided for generating a base visual score for each candidate image of a plurality of images received by a computing system, based on the scene type of each image. For each candidate image, the computing system multiplies the base visual score by a feature importance weight to generate a first visual score, adds respective scene type bonus points to the first visual score to generate a second visual score, and adds diversity scoring points to the second visual score to generate a final visual score for each candidate image. The computing system ranks the candidate images based on the final visual scores and provides a specified number of the top-ranked candidate images to be displayed on a display of the computing device.

Abstract: Systems and methods are provided for receiving a plurality of images corresponding to a listing in an online marketplace, generating a scene type for each image of the plurality of images, and grouping each image into a scene type group of a set of predefined scene types. Each group of images are input into a respective machine learning model specific to the scene type of the group of images to generate a visual score for each image in each group of images, and an attractiveness score is generated for the listing in the online marketplace based on the visual scores for each image in each group of images.

Abstract: Systems and methods herein describe a listing auto-generation system for generating a listing title and listing description for a listing in an online marketplace. The listing auto-generation system analyzes a set of listing data associated with the listing and generates the listing title and listing description using a machine learning model. The generated listing title and listing description are validated against a set of validation rules and presented on a user interface.

Abstract: Systems and methods are provided for receiving a plurality of images corresponding to a listing in an online marketplace, generating a scene type for each image of the plurality of images, and grouping each image into a scene type group of a set of predefined scene types. Each group of images are input into a respective machine learning model specific to the scene type of the group of images to generate a visual score for each image in each group of images, and an attractiveness score is generated for the listing in the online marketplace based on the visual scores for each image in each group of images.

Abstract: Systems and methods are provided for generating a base visual score for each candidate image of a plurality of images received by a computing system, based on the scene type of each image. For each candidate image, the computing system multiplies the base visual score by a feature importance weight to generate a first visual score, adds respective scene type bonus points to the first visual score to generate a second visual score, and adds diversity scoring points to the second visual score to generate a final visual score for each candidate image. The computing system ranks the candidate images based on the final visual scores and provides a specified number of the top-ranked candidate images to be displayed on a display of the computing device.