Abstract: A computer-implemented method for determining image similarity includes determining, by a first neural network, a first feature value associated with a first characteristic of a first product based on an image of the first product. The method also includes determining, by a second neural network, a second feature value associated with a second characteristic of the first product based on the image of the first product. The method further involves calculating a first vector space distance between the first feature value and a third feature value associated with the first characteristic of a second product, and calculating a second vector space distance between the second feature value and a fourth feature value associated with the second characteristic of the second product. Additionally, the method includes determining a similarity value based on the first vector space distance and the second vector space distance.

Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.

Abstract: A method includes determining a first taxonomy of an anchor product. The first taxonomy includes a plurality of levels for classifying products organized from a highest taxonomy level to a lowest level. The method further includes determining a second taxonomy closest to the first taxonomy. The second taxonomy is associated with a group of products, the first taxonomy and the second taxonomy have at least a common highest taxonomy level, and the determination is made at least in part based on co-purchase data indicating that the anchor product and at least one product in the group of products are purchased together more often than products associated with other taxonomies are purchased with the anchor product. The method further includes determining a most similar product to the anchor product from the group of products of the second taxonomy and associating the anchor product and the most similar product in a collection.

Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.

Abstract: A computer-implemented method for determining image similarity includes determining, by a first neural network, a first feature value associated with a first characteristic of a first product based on an image of the first product. The method also includes determining, by a second neural network, a second feature value associated with a second characteristic of the first product based on the image of the first product. The method further involves calculating a first vector space distance between the first feature value and a third feature value associated with the first characteristic of a second product, and calculating a second vector space distance between the second feature value and a fourth feature value associated with the second characteristic of the second product. Additionally, the method includes determining a similarity value based on the first vector space distance and the second vector space distance.

Abstract: A method includes determining a first taxonomy of an anchor product. The first taxonomy includes a plurality of levels for classifying products organized from a highest taxonomy level to a lowest taxonomy level. The method further includes determining a second taxonomy closest to the first taxonomy. The second taxonomy is associated with a group of products, the first taxonomy and the second taxonomy have at least a common highest taxonomy level, and the determination is made at least in part based on co-purchase data indicating that the anchor product and at least one product in the group of products are purchased together more often than products associated with other taxonomies are purchased with the anchor product. The method further includes determining a most similar product to the anchor product from the group of products of the second taxonomy and associating the anchor product and the most similar product with one another in a product collection.

Abstract: The present disclosure includes a system, method, and article of manufacture for generating an entity graph. The method may comprise determining a relationship between a first entity and a second entity based upon internal data, external data, and/or online data associated with the first entity, and generating the entity graph comprising at least two nodes and an edge connecting the at least two nodes. The method may further comprise, in various embodiments, tailoring marketing to the first entity based upon the entity graph, detecting fraud against the first entity based upon the entity graph, periodically updating the entity graph based upon new internal data and new online data, and/or adjusting the edge based upon a change in the relationship between the first entity and the second entity.

Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.

Abstract: A computer-implemented method for determining image similarity includes determining, by a first neural network, a first feature value associated with a first characteristic of a first product based on an image of the first product. The method also includes determining, by a second neural network, a second feature value associated with a second characteristic of the first product based on the image of the first product. The method further involves calculating a first vector space distance between the first feature value and a third feature value associated with the first characteristic of a second product, and calculating a second vector space distance between the second feature value and a fourth feature value associated with the second characteristic of the second product. Additionally, the method includes determining a similarity value based on the first vector space distance and the second vector space distance.

Abstract: A computer-implemented method for determining image similarity includes determining, by a first neural network, a first feature value associated with a first characteristic of a first product based on an image of the first product. The method also includes determining, by a second neural network, a second feature value associated with a second characteristic of the first product based on the image of the first product. The method further involves calculating a first vector space distance between the first feature value and a third feature value associated with the first characteristic of a second product, and calculating a second vector space distance between the second feature value and a fourth feature value associated with the second characteristic of the second product. Additionally, the method includes determining a similarity value based on the first vector space distance and the second vector space distance.

Abstract: A computer-implemented method includes extracting, by one or more processors of one or more computing devices, a product family name from each of a plurality of unstructured product titles associated with a plurality of products. The method further includes determining, by the one or more processors, a degree of similarity between model numbers of the plurality of products. The method further includes determining, by the one or more processors, that at least two of the plurality of products are variants of one another by determining that the at least two of the plurality of products have a same extracted product family name and determining that the degree of similarity between the model numbers of the plurality of products is above a predetermined threshold.

Abstract: This disclosure includes systems and methods for providing purchase recommendations to a user that may include items frequently purchased with a product selected by the user. The determination of which items are frequently purchased with which other items may account for both online and in-store transactions and may further account for both pairwise and multi-wise relationships. The recommendations may be provided on an electronic user interface, such as a website, in response to the user's selection of the product through the electronic user interface. The recommendations may be tailored to the user's selected product so that the recommended items are available in the same delivery channel as the user-selected product.

Abstract: A computer-implemented method for determining image similarity includes determining, by a first neural network, a first feature value associated with a first characteristic of a first product based on an image of the first product. The method also includes determining, by a second neural network, a second feature value associated with a second characteristic of the first product based on the image of the first product. The method further involves calculating a first vector space distance between the first feature value and a third feature value associated with the first characteristic of a second product, and calculating a second vector space distance between the second feature value and a fourth feature value associated with the second characteristic of the second product. Additionally, the method includes determining a similarity value based on the first vector space distance and the second vector space distance.

Abstract: This disclosure includes systems and methods for providing purchase recommendations to a user that may include items frequently purchased with a product selected by the user. The determination of which items are frequently purchased with which other items may account for both online and in-store transactions and may further account for both pairwise and multi-wise relationships. The recommendations may be provided on an electronic user interface, such as a website, in response to the user's selection of the product through the electronic user interface. The recommendations may be tailored to the user's selected product so that the recommended items are available in the same delivery channel as the user-selected product.

Abstract: Systems and methods for providing suggestions of complementary products responsive to an anchor product are disclosed. The method includes receiving a selection of an anchor product. A similarity score between text embeddings of the anchor product and text embeddings of a plurality of products in a product database is calculated. A similarity score between an image feature of the anchor product and an image feature of the plurality of products in the product database is calculated. A weighted score between the two similarity scores as calculated for the anchor product and the plurality of products in the product database is calculated. At least one of the products from the product database having a highest weighted score is selected and returned responsive to the selection of the anchor product.

Abstract: A method for machine learning-based classification may include training a machine learning model with a full training data set, the full training data set comprising a plurality of data points, to generate a first model state of the machine learning model, generating respective embeddings for the data points in the full training data set with the first model state of the machine learning model, applying a clustering algorithm to the respective embeddings to generate one or more clusters of the embeddings, identifying outlier embeddings from the one or more clusters of the embeddings, generating a reduced training data set comprising the full training data set less the data points associated with the outlier embeddings, training the machine learning model with the reduced training data set to a second model state, and applying the second model state to one or more data sets to classify the one or more data sets.

Abstract: A method includes determining a first taxonomy of an anchor product. The first taxonomy includes a plurality of levels for classifying products organized from a highest taxonomy level to a lowest taxonomy level. The method further includes determining a second taxonomy closest to the first taxonomy. The second taxonomy is associated with a group of products, the first taxonomy and the second taxonomy have at least a common highest taxonomy level, and the determination is made at least in part based on co-purchase data indicating that the anchor product and at least one product in the group of products are purchased together more often than products associated with other taxonomies are purchased with the anchor product. The method further includes determining a most similar product to the anchor product from the group of products of the second taxonomy and associating the anchor product and the most similar product with one another in a product collection.

Abstract: This disclosure includes systems and methods for providing accessory product recommendations to a user that may include items related to an anchor product selected by the user. The determination of accessory product recommendations may be made based on compatibility between anchor and accessory products, based on manually entered anchor-accessory product relationships, using an algorithm trained to determine anchor-accessory product relationships, or using other methods. The accessory recommendations may be provided along with the anchor product on an electronic user interface, such as a website, in response to the user's selection of the product through the electronic user interface. In this way, the accessory and anchor products may be viewed and/or selected by the user with a minimal number of clicks or user interactions through the electronic user interface.

Abstract: A scheduling system and method for high-performance computing (HPC) applications includes a network management component stored in physical memory and executed by a processor. The management component is configured to transform HPC resources into a schedulable resource catalog by transforming multi-dimensional HPC resources into a one dimension versus time resource catalog with a dependent graph structure between resources such that HPC resources are enabled to be provisioned into a service environment with predictable provisioning using the resource catalog. A graphical user interface component is coupled to the network management component and configured to provide scheduling visibility to entities and to enable a plurality of different communication modes for scheduling and communication between entities.

Abstract: A method of providing purchase recommendations to a user may include tracking user comparisons of products on an electronic commerce system, receiving a user selection of an anchor product from the products through an electronic user interface of the electronic commerce system, designating a recommended product from the products for recommendation to the user through the electronic commerce system according to a frequency with which the recommended product is compared with the anchor product based on the tracking, and presenting the designated recommended product to the user responsive to the user's selection of the anchor product.