Abstract: In various examples, a system receives image data characterizing an image of an item. Additionally, the system implements a first set of operations and a second set of operations. In some examples, the first set of operations includes performing a structural similarity analysis of the item, based on the image data, and determining a structural similarity score based on the structural similarity analysis of the item. In other examples, the second set of operations includes generating a plurality of derivative images by applying a plurality of distortions to the image of the item, extracting one or more features based at least on the plurality of derivative images, and determining the quality of the image based at least on the extracted one or more features and the structural similarity score.

Abstract: In various examples, a system receives image data characterizing an image of an item. Additionally, the system implements a first set of operations and a second set of operations. In some examples, the first set of operations includes performing a structural similarity analysis of the item, based on the image data, and determining a structural similarity score based on the structural similarity analysis of the item. In other examples, the second set of operations includes generating a plurality of derivative images by applying a plurality of distortions to the image of the item, extracting one or more features based at least on the plurality of derivative images, and determining the quality of the image based at least on the extracted one or more features and the structural similarity score.

Abstract: In various examples, a system receives image data characterizing an image of an item. Additionally, the system implements a first set of operations and a second set of operations. In some examples, the first set of operations includes performing a structural similarity analysis of the item, based on the image data, and determining a structural similarity score based on the structural similarity analysis of the item. In other examples, the second set of operations includes generating a plurality of derivative images by applying a plurality of distortions to the image of the item, extracting one or more features based at least on the plurality of derivative images, and determining the quality of the image based at least on the extracted one or more features and the structural similarity score.

Abstract: A disclosed sentiment topic modeling tool identifies issues within voluminous customer review data, based on particular categories of review submission (e.g., particular products and experiences) and concern areas (e.g., quality, performance, suitability of features), and abstracts the extracted topic data into a manageable set of focus areas for business operations improvements. An exemplary process includes: receiving a plurality of reviews; selecting a category of review to use for a topic network; selecting a number of topics for generating the topic network; generating, based at least on the selected category and the selected number of topics, the topic network; generating a plurality of topic networks in a topic network group, and determining a set of themes for the group. Additional network groups are generated, and a set of themes is determined for each. A set of focus areas is determined, based at least on the sets, and reports are generated.

Abstract: Systems, methods, and computer-readable storage media for cataloguing and assessing images. This is performed by a system which receives images of an item, and identifying, within each image, the item. The system performs a structural similarity analysis of the item and for each image applies a plurality of distortions, such that for each image in the images multiple distorted images are generated. The system identifies within the distorted images at least one feature and applies a regression model to the images using the at least one feature and the structural similarity score.

Abstract: A disclosed sentiment topic modeling tool identifies issues within voluminous customer review data, based on particular categories of review submission (e.g., particular products and experiences) and concern areas (e.g., quality, performance, suitability of features), and abstracts the extracted topic data into a manageable set of focus areas for business operations improvements. An exemplary process includes: receiving a plurality of reviews; selecting a category of review to use for a topic network; selecting a number of topics for generating the topic network; generating, based at least on the selected category and the selected number of topics, the topic network; generating a plurality of topic networks in a topic network group, and determining a set of themes for the group. Additional network groups are generated, and a set of themes is determined for each. A set of focus areas is determined, based at least on the sets, and reports are generated.

Abstract: Examples provide a system for performing retail-based cost reverse engineering and cost comparison within item similarity clusters for cost negotiations associated with a plurality of items. A cost manager component creates item similarity clusters based on item descriptions and cost trends associated with items over time. The cost manager component identifies items for action recommendations based on quantity and margin for each item. The recommendations can include a recommended set of item suitable for negotiations, promotional activities and/or a set of substitute items recommended for replacement of a selected item in an item assortment.

Abstract: Systems, methods, and computer-readable storage media for cataloguing and assessing images. This is performed by a system which receives images of an item, and identifying, within each image, the item. The system performs a structural similarity analysis of the item and for each image applies a plurality of distortions, such that for each image in the images multiple distorted images are generated. The system identifies within the distorted images at least one feature and applies a regression model to the images using the at least one feature and the structural similarity score.