Home Depot Product Authority, LLC

A method includes retrieving, by a processor in an image classification system, a plurality of product images from a memory. A first image classifier is applied to a first of the plurality of product images. The first of the plurality of product images is associated with a first category. A second image classifier is applied to a second of the plurality of product images. The second of the plurality of product images is associated with a second category. A first result of the first image classifier for the first of the plurality of product images is stored in the memory. A second result of the second image classifier for the second of the plurality of product images is stored in the memory.

A method for providing document category recommendations may include training a machine learning algorithm; receiving, from a user, a selection of an anchor document; retrieving a user co-viewing sequence; generating, via the trained machine learning algorithm, a sequence embeddings set based on the anchor document and the user co-viewing sequence; comparing the generated embeddings set to respective embeddings for a plurality of candidate sets; determining the candidate set closest to the generated embeddings; and presenting at least one category from the closest candidate set.

A method includes receiving a new image of an item based on a time-stamped user interaction with the item, applying object detection to the new image to identify the item, determining a respective similarity of the image to each old image of the item in a library of old images, and adding the new image to the library only if each respective similarity is below a similarity threshold.

Systems and methods for training a first model using reinforcement learning can include, for a first input sequence of one or more first input sequences, obtaining the first input sequence, predicting, by a first model, a set of candidate items as recommendations based on the first set of items, sending the set of candidate items to a user computing device, obtaining feedback data corresponding to a second input sequence, determining a first value for a first item and a second value for a second item, the first value and the second value representative of predictive probabilities of the first model, determining a training dataset including a plurality of data points, each data point including a weighted score, and training the first model using the training dataset to update one or more parameters of the first model to minimize a prediction loss by the first model.

Systems and methods for payment processing include receiving, by a payment terminal, a personal account number to complete a payment. The personal account number is encrypted by the payment terminal. The encrypted personal account number is sent from the payment terminal to a merchant server via a network. The encrypted personal account number is sent from the merchant server to a tokenization service provider server for tokenization and validation via a payment processor. The merchant server receives an indication of whether the transaction was successful and a token from the tokenization service provider server.

A system or method for identifying key drivers of change in a dataset based on a metric includes obtaining a first dataset including a plurality of data objects having feature values for a plurality of features, pruning one or more data objects from the first dataset based on feature values associated with each data object and the metric and determining a second dataset including a set of data objects having associated therewith feature values for the plurality of features, determining, based on the metric, a candidate feature in the second dataset, identifying, based on a threshold value, a candidate feature value representative of a key driver of change from feature values associated with the candidate feature in the second dataset, the candidate feature value being representative of the key driver of the first dataset as output in response to the query, and the metric including the threshold value.

A computer-implemented method for access control includes detecting, by a security agent of a computing device, a request by a user of the computing device for access to a controlled asset. The method also includes authenticating, by the security agent, the user in response to the request. The method includes granting, by the security agent, access to the controlled asset based on determining that an initial risk score for the authenticated user does not exceed a predetermined threshold. Additionally, the method includes periodically calculating, by the security agent using at least one security policy and at least one machine learning model, an updated risk score of the authenticated user based on a behavior of the user. The method further includes performing a security action in response to determining the updated risk score exceeds the predetermined threshold. Various other methods, systems, and computer-readable media are also disclosed.

A countertop system includes a first slab having a first lateral slot that is laterally obscured by a first cover, and a second slab having a second lateral slot that is laterally obscured by a second cover. The first and second covers are configured to be removed with an application of pressure, and the first slot aligns with the second slot to receive a connector to couple the first slab to the second slab.

Systems and methods for payment processing include receiving, by a payment terminal, a personal account number to complete a payment. The personal account number is encrypted by the payment terminal. The encrypted personal account number is sent from the payment terminal to a merchant server via a network. The encrypted personal account number is sent from the merchant server to a tokenization service provider server for tokenization and validation via a payment processor. The merchant server receives an indication of whether the transaction was successful and a token from the tokenization service provider server.