Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: training, using labeled training data and a list of substitutes for an item, a machine learning algorithm; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of substitutes; ranking each substitute of the list of substitutes based on its respective similarity score; and re-training the machine learning algorithm based on at least the labeled training data and a highest ranked substitute of the list of substitutes. Other embodiments are disclosed herein.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: determining a list of possible substitutes for an item when the item is out of stock; training a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item as output; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of possible substitutes; determining a respective historical substitution score for each possible substitute; determining a respective final score for each possible substitute comprises using at least one or more rectifiers having ReLU non-linearity; ranking each possible substitute; storing a selection of the highest ranked possible substitute as additional training data with the labeled training data; and re-training the machine learning algorithm

Abstract: A recommender system can include a defender computing device that is configured to obtain customer interaction data characterizing customer interactions with an ecommerce marketplace. The defender computing device can also be configured to determine an item recommendation based on the customer interaction data using a trained differentially private recommendation model and send the item recommendation to the customer. The trained differentially private recommendation model is more likely to determine the same item recommendation after poisoned data is injected into the customer interaction data than a recommendation model that is not privately trained.

Abstract: This application relates to apparatus and methods for automatically detecting attacks to advertisement systems. In some examples, a computing device trains a machine learning process based on a training dataset. The training dataset may be an identified portion of a website session dataset that includes a lower percentage of malicious data caused by attacks than other portions, or may include no malicious data. Once trained, the computing device generates features from a website session dataset for a customer, and applies the trained machine learning process to the generated features to detect malicious data within the website session dataset for the customer. Further, the computing device may filter the website session data to remove the detected malicious data, and may store the filtered website session data within a data repository. The computing device may provide the filtered website session data to a recommendation system to generate item recommendations for the customer.

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: In some examples, a system may be configured to, implement a first set of operations that generate a first set of data characterizing an importance of the most recently added anchor item to the user. Further, the system may be configured to, implement a second set of operations that generate a second set of data characterizing a likelihood of an occurrence of a substitution rejection event associated with the user. That way, based on the first set of data and the second set of data, the system may be configured to generate output data, and implement a set of notification operations based on the output data.

Abstract: An optimized substitution system can include a computing device that is configured to receive a notification indicating an order and a first item identifier and, based on the first item identifier, identify a set of substitute item identifiers. Each substitute item identifier of the set of substitute item identifiers includes a probability value using an optimization computation based on a similarity and a value of the corresponding set of substitute item identifiers and the first item identifier. The similarity and the value are stored in a database. The compute device is also configured to, based on the probability values corresponding to the set of substitute item identifiers, select a first substitute item identifier of the set of substitute item identifiers and update the order to include the first substitute item identifier and exclude the first item identifier.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: determining a list of possible substitutes for an item when the item is out of stock; training, a machine learning algorithm, using labeled training data as input and a list of possible substitutes for the item as output; determining, using the machine learning algorithm, as trained, a respective similarity score for each substitute of the list of possible substitutes; determining a respective historical substitution score for each possible substitute; determining a respective final score for each possible substitute comprises using at least one or more rectifiers having ReLU non-linearity; ranking each possible substitute; storing a selection of the highest ranked possible substitute as additional training data with the labeled training data; and re-training the machine learning algorith

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: A privacy system includes a computing device configured to obtain user transactional data characterizing at least one transaction of a user on an ecommerce marketplace and to determine a privacy vulnerability score of the user by comparing the transactional data to a user vulnerability distribution. The computing device is also configured to send the privacy vulnerability score to a personalization engine.

Abstract: A recommender system can include a defender computing device that is configured to obtain customer interaction data characterizing customer interactions with an ecommerce marketplace. The defender computing device can also be configured to determine an item recommendation based on the customer interaction data using a trained differentially private recommendation model and send the item recommendation to the customer. The trained differentially private recommendation model is more likely to determine the same item recommendation after poisoned data is injected into the customer interaction data than a recommendation model that is not privately trained.

Abstract: This application relates to apparatus and methods for automatically detecting attacks to advertisement systems. In some examples, a computing device trains a machine learning process based on a training dataset. The training dataset may be an identified portion of a website session dataset that includes a lower percentage of malicious data caused by attacks than other portions, or may include no malicious data. Once trained, the computing device generates features from a website session dataset for a customer, and applies the trained machine learning process to the generated features to detect malicious data within the website session dataset for the customer. Further, the computing device may filter the website session data to remove the detected malicious data, and may store the filtered website session data within a data repository. The computing device may provide the filtered website session data to a recommendation system to generate item recommendations for the customer.

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: determining a list of substitutes for an item; determining qualities for each substitute; determining a similarity score for each substitute; determining a historical substitution score for each substitute; determining a final score for each substitute using the similarity score for each substitute and the historical substitution score for each substitute; ranking each substitute based upon the final score for each substitute; facilitating a display, on a user interface of a user device, of a highest ranked substitute; receiving, from the user interface of the user device, a selection of the highest ranked substitute; and after receiving the selection of the highest ranked substitute, substituting the highest ranked substitute for the item of the list of items. Other embodiments are disclosed herein.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: A system includes a computing device configured to obtain item attribute data that corresponds to a characteristic of an item ordered by a customer on an e-commerce platform and a common characteristic of a plurality of substitution items. The computing device is also configured to obtain customer attribute data identifying preferences of the customer and to determine a preference score for each substitution item in the plurality of substitution items based on the item attribute data and the customer attribute data. The preference score indicates a likelihood that the customer will accept one of the plurality of substitution items as a replacement for the item ordered by the customer. The computing device is also configured to rank each substitution item in the plurality of substitution items based on the preference scores.

Abstract: A system includes a computing device configured to receive order data indicating an order placed by a customer on an e-commerce platform and route the order into a test group or a control group when the order data indicates that the order will be filled by a store participating in the comparison test. The computing device is further configured to apply test features to the order if the order was routed into the test group and apply control features to the order if the order was routed into the control group and determine recommended substitute items based on the test features or the control features. The recommended substitute items are intended to replace items ordered by the customer that are unavailable. The computing device is also configured to determine one or more performance metrics of the test group and the control group.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.

Abstract: This application relates to apparatus and methods for automatically identifying substitute items. A computing device can generate matrix data that identifies connection values between a plurality of items. The matrix data may be generated based on the application of one or more machine learning algorithms to historical data identifying accepted or denied item substitutions. The computing device may then receive item data identifying at least one second item and at least one attribute of that second item. The computing device may generate a graph based on the matrix data and the item data to determine connection values between the second item and the plurality of first items. The computing device may then determine a substitute item (e.g., a replacement item) for the second item based on the connection values between the second item and the plurality of first items.