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 network device may receive, from an endpoint device, a first message that includes first endpoint identification information. The network device may be connected to the endpoint device via a plurality of links. The network device may receive, from another network device, a second message that includes second endpoint identification information. The network device may determine whether the first endpoint identification information corresponds to the second endpoint identification information. The network device may cause, based on determining whether the first endpoint identification information corresponds to the second endpoint identification information, a state of the plurality of links to be maintained or changed.

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: An approach is disclosed for recommending complementary items based on customer shopping routines. The approach receives anchor item data. The approach identifies a routine that corresponds to the anchor item data. The routine is based on an item purchasing behavior of a customer. The approach determines categorical data within the identified routine by applying a ranking algorithm to the categorical data of the categories and the anchor item data. The categorical data is relevant to the anchor item data. The approach generates relevant item data from the categorical data by applying an item recommendation model to item data that corresponds to the categorical data.

Abstract: An approach is disclosed for recommending trending complementary items or trending similar items. The approach receives anchor item data corresponding to an anchor item. The approach determines a subcategory data corresponding to a category of the anchor item. The approach identifies an attribute label of the anchor item subcategory data, in which the attribute label indicates whether the anchor item is eligible for up-selling. The approach identifies, in response to the attribute label indicating that the anchor item is not eligible for up-selling, complementary subcategory data corresponding to the anchor item subcategory data, based on historical transaction data and at least one of co-view data and add-to-cart data. The approach generates recommended cross-selling item data from the complementary subcategory data, the recommended cross-selling item data being generated by applying a trending model to historical transaction data of items having complementary subcategory data.

Abstract: In many embodiments, a system comprising one or more processor and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform: determining a set of questions associated with a campaign by extracting text from one or more advertisements; generating vector embeddings of one or more online activities of a user; receiving a search query from a graphical user interface of a computing device of the user; determining a second question from the set of questions to present to the user based on respective relevance probabilities; determining a confidence score associated with the second question; and when the confidence score associated with the second question exceeds a second predetermined threshold, presenting the second question to the user via the graphical user interface. Other embodiments of related methods and systems are also provided.

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 providing a unified serving platform that allows for the reusability of machine learning models across a plurality of websites to determine personalized content. For example, a computing device trains a machine learning model with session data identifying browsing events and transaction data identifying purchasing events for a plurality of users. The computing device receives and stores session data and transaction data associated with a first website for the customer. The computing device may then receive a request for content to display to the customer on a second website. The computing device generates label data based on the session data and transaction data associated with the first website, and executes the trained machine learning model with the label data. Based on execution of the trained machine learning model, the computing device generates content to display on the second website, and transmits the content.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform: gathering first data comprising first interactions of a user with a first graphical user interface; storing the first data comprising the first interactions of the user with the first graphical user interface as at least one first vector by adding to the at least one first vector for each level of a hierarchical categorization of the first user interface; gathering second data comprising second interactions of the user with a second graphical user interface; storing the second data comprising the second interactions of the user with the second graphical user interface as at least one second vector; determining an intent of the user using the at least one first vector, the at least one second vector, and a predictive algorithm; and transmitting instructions to display a third graphical user inter

Abstract: In many embodiments, the method can comprise receiving a search query from a search by a user and determining a question to present to the user. In many embodiments, determining the question to present to the user can comprise evaluating a user profile associated with the user, evaluating the search query, evaluating one or more user actions during a current browse session of the user, and selecting the question from a set of questions. In some embodiments, the method can further comprise presenting the question to the user when a confidence score associated with the question reaches or exceeds a predetermined threshold. Other embodiments of related methods and systems are also provided.

Abstract: System and method for generating a ranked list are disclosed. A plurality of prior interactions for a first customer are received by a computing device. Each of the prior interactions includes a product interaction and time. A ranked list of item recommendations is generated based on the plurality of prior interactions. The ranked list of item recommendations is generated by a trained prediction model trained using temporal information embedded into a finite-dimensional vector space. The ranked list of item recommendations is output by the computing device.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform acts of: gathering historical data, which can comprise interactions of a user with a first graphical user interface at a first time; storing the historical data which can comprise the interactions of the user with the first graphical user interface as at least one historical feature vector; gathering in-session data, which can comprise interactions of the user with a second graphical user interface at a second time later than the first time; storing the in-session data which can comprise the interactions of the user with the second graphical user interface as at least one in-session feature vector; determining an intent of the user using the at least one historical feature vector and the at least one in-session feature vector; and transmitting instructions to display a third graphical user interf

Abstract: In accordance with various embodiments, an electron beam evaporator can comprise the following: a tubular target; an electron beam gun for producing at least one vapor source on a removal surface of the tubular target by means of an electron beam; wherein the removal surface is a ring-shaped axial end surface or a surface of the tubular target that extends conically or in a curved fashion from the free end edge.

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 including one or more processors and one or more non-transitory media storing computer instructions configured to run on the one or more processors and perform: identifying a first sub-population of case individuals from a gross population of the case individuals; presenting first test content to a first test sub-population of the case individuals, the first test content is selected according to a first statistical model; measuring a first test sub-population average feedback metric based on first test content feedback provided from the first test sub-population of the case individuals; determining that the first test sub-population average feedback metric exceeds a first control population average feedback metric of a first control population of the case individuals; and determining that a probability value for a difference between the first test sub-population average feedback metric and the first control population average feedback metric is less than a predetermined significance level value.

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 acts of: determining a list of suitable substitutes for the item of the list of items, wherein the list of suitable substitutes can comprise a set of items in an item catalogue; determining qualities for each suitable substitute of the list of suitable substitutes; determining a similarity score for each suitable substitute of the list of suitable substitutes; determining a historical substitution score for each suitable substitute of the list of suitable substitutes; determining a final score for each suitable substitute of the list of suitable substitutes using the similarity score for each suitable substitute of the list of suitable substitutes and the historical substitution score for each suitable substitute of the list of suitable substitutes; ranking each suitable substitute of the list of suitable substitutes base

Abstract: Identifying one or more sub-populations of case individuals from a gross population of case individuals can comprise identifying the one or more sub-populations of case individuals. Case individuals of the first sub-population of case individuals are associated with at least one first sub-population feature. The first case individuals are exclusive from the second case individuals. The first case individuals and the second case individuals together comprise the case individuals of the first sub-population of case individuals presenting first control content to case individuals of the first control sub-population. The first control content is selected according to a first statistical model measuring an average feedback metric of the case individuals of the first control sub-population provided in response to being presented the first control content presenting first test content to case individuals of the first test sub-population.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform acts of: gathering historical data, which can comprise interactions of a user with a first graphical user interface at a first time; storing the historical data which can comprise the interactions of the user with the first graphical user interface as at least one historical feature vector; gathering in-session data, which can comprise interactions of the user with a second graphical user interface at a second time later than the first time; storing the in-session data which can comprise the interactions of the user with the second graphical user interface as at least one in-session feature vector; determining an intent of the user using the at least one historical feature vector and the at least one in-session feature vector; and transmitting instructions to display a third graphical user interf

Abstract: A system can include one or more processing modules and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processing modules and perform receiving, from an electronic device, a search query from a user of a plurality of users; processing first data; and facilitating displaying a set of items. Processing the first data can comprise determining one or more keywords by capturing the one or more keywords during a time window; creating a feature set of second data associated with at least a portion of the plurality of users; determining a set of items of the item set as being based at least in part on an item vector representation and a keyword vector representation; determining a respective purchase probability associated with each item of the set of items of the item set; ranking the set of items.