Abstract: Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.

Abstract: Disclosed herein is a method of provisioning a virtual experience of a building based on user preference. The method may include receiving an identity data associated with an identity of a user, retrieving a user profile data based on the identity data, analyzing the user profile data using a machine learning model, determining at least one preference data based on the analyzing, identifying at least one virtual utility object based on the at least one preference data, generating an interactive 3D model data comprising the at least one virtual utility object, transmitting the interactive 3D model data to a user device configured to present the interactive 3D model data, receiving a reaction data from the user device. Further, the user device may include at least one sensor configured to generate the reaction data based on a behavioral reaction of a user consuming the interactive 3D model data.

Abstract: This application relates to apparatus and methods for automatically determining and providing item advertisements to customers. In some examples, a computing device obtains transaction data identifying in-store and/or online transactions. The computing device determines a distribution of purchased items over a plurality of item categories based on the transaction data. The computing device generates factorization matrices based on applying a machine learning process to the distribution, and generates relevancy scores for the plurality of item categories based on the factorization. The computing device may then select or generate item advertisements for items associated with the item categories based on the generated relevancy scores. The selected item advertisements may be displayed to a customer, for example, on a website.

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 system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform generating one or more item relational graphs for one or more items based on historical user purchases; transforming, using spectral filtering, the one or more item relational graphs into one or more frequency signals to remove noise from the one or more frequency signals; constructing, using a machine learning model, one or more item pair label classifications for one or more item pairs of the one or more items; generating a respective similarity score for each of the one or more item pairs; outputting a top k results for the one or more item pairs ranked by the respective similarity scores; and re-ranking, using a re-ranking algorithm, the top k results of the one or more item pairs based on a user preference for display on a user interface of an electronic device of a user. Other embodiments are disclosed.

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, perform certain acts. The acts can include obtaining training data. The acts also can include training candidate recommendation models and an adversarial exposure model using the training data. The acts additionally can include generating recommendations based on a selected recommendation model of the candidate recommendation models. Other embodiments are described.

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: generating one or more attribute affinity scores for one or more attributes associated with an item type category, wherein the one or more attribute affinity scores predict a user's affinity for attribute values associated with the one or more attributes; generating a respective attribute importance score for each of the one or more attributes, the respective attribute importance score predicting a respective importance of each of the one or more attributes to the user; and generating personalized search results that are ordered based, at least in part, on the one or more attribute affinity scores and the respective attribute importance scores. Other embodiments are disclosed herein.

Abstract: A method of obtaining item recommendations associated with an anchor item chosen by a user via a user interface executed on a user device of the user. The method further can include determining an anchor label for the anchor item. In a number of embodiments, the anchor label can be determined based at least in part on one or more features of an anchor category of the anchor item. The method additionally can include determining a personalized recommendation strategy for the user based at least in part on a user mode for the user and the anchor label. The method further can include re-ranking the item recommendations based at least in part on the personalized recommendation strategy. The method also can include transmitting the item recommendations re-ranked to be displayed with the anchor item on the user interface. Other embodiments are disclosed.

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: providing a machine learning architecture that is configured to evaluate expensiveness of items relative to each other, wherein the items are included in an item type category; receiving prices associated with the items included in the item type category; generating, using a price band determination model associated with the machine learning architecture, price bands based, at least in part, on the prices associated with the items, each of the price bands being associated with separate price range boundaries for the item type category; and assigning each of the items to one of the price bands. Other embodiments are disclosed herein.

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: providing a search engine that includes, or communicates with, a recall personalization model configured to generate personalized recall sets of search results for users; receiving, at the search engine, a search query submitted by a user; generating, using the recall personalization module, a feature vector for the user that includes contextual features associated with the user; generating, using the recall personalization model, a simulated narrowing query that includes the search query submitted by the user and the feature vector; generating, using the search engine, a recall set of search results based, at least in part, on the simulated narrowing query. Other embodiments are disclosed herein.

Abstract: A method of obtaining item recommendations associated with an anchor item chosen by a user via a user interface executed on a user device of the user. The method further can include determining an anchor label for the anchor item. In a number of embodiments, the anchor label can be determined based at least in part on one or more features of an anchor category of the anchor item. The method additionally can include determining a personalized recommendation strategy for the user based at least in part on a user mode for the user and the anchor label. The method further can include re-ranking the item recommendations based at least in part on the personalized recommendation strategy. The method also can include transmitting the item recommendations re-ranked to be displayed with the anchor item on the user interface. Other embodiments are disclosed.

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 method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform creating a standardized flavor list from flavors associated with items in a catalog; determining a respective score indicating a degree of affinity for a user for each flavor of each of one or more item types associated with the items; creating a respective ordered list of flavors for each of the one or more item types for the user in which the respective ordered list of flavors are ranked by the respective score for the each flavor in the ordered list; and re-ranking a non-personalized list of recommended items associated with an anchor item of the items using the respective ordered list of flavors for the user. Other embodiments are disclosed.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform generating personalized product-type metrics for the user based at least in part on a user embedding for the user and product-type embedding Gaussian mixture distributions; determining top product types based at least in part on personalized product-type complementarity metrics generated using the personalized product-type metrics and cosine similarity measurements; generating a set of first items associated with the top product-types; ranking each respective item in the set of first items generated using an item-level embedding Gaussian distribution for the anchor item and a respective item-level embedding Gaussian distribution for the each respective item; and selecting a set of top items as the personalized complementary item recommendations based on the ranking. Other embodiments are disclosed.

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 receiving a sequence of actions entered into a GUI before an account action for a user account of a user during a user session initiated via a first user portal; dividing the sequence of actions into actions for real-time features and actions for periodic features; simultaneously (1) storing the actions for the periodic features in a distributed cloud storage system and (2) processing the actions for the real-time features into the real-time features; and simultaneously storing the periodic features and the real-time features in a database managed by real-time stream processing software.

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 price bands for an item type category; associating items included in the item type category with the price bands; analyzing, using an affinity prediction model of the machine learning architecture, price band activity data indicating interactions of a user with respective items included in each of the price bands for the item type category; and generating one or more price affinity predictions for the user based, at least in part, on the price band activity data, wherein the one or more price affinity predictions predict a preference of the user for respective items associated with one or more of the price bands. Other embodiments are disclosed herein.

Abstract: A method including a user who has brand affinities about a product brand or a product brand category. The method can include analyzing whether the user has brand affinities for the product brand or the brand category with two binary classification models that can include (a) a model one to determine an affinity to a particular product brand and (b) a model two to determine an affinity to a particular product brand category. The method also can include analyzing whether the user has affinities for a similar product brand or a similar product brand category by a model three neural network. The method can include displaying recommendations and the promotions for a product brand, a product brand category, a similar product brand, or a similar product brand category to the user. Other embodiments are disclosed.

Abstract: This application relates to apparatus and methods for automatically determining and providing item category advertisement recommendations. In some examples, a computing device obtains transaction data identifying historical transactions. The computing device generates a first model, and trains the first model with non-seasonal data. The computing device generates a second model, and trains the second model with seasonal data. The computing device then generates a seasonal re-rank model based on the first model and the second model. The seasonal re-rank model, when executed, identifies probabilities of purchase of categories of items. In some examples, the computing device selects item advertisements to provide for display to a customer based on the probabilities of purchase of categories of items determined by the seasonal re-rank model. The selected item advertisements may be displayed to the customer, for example, on a website.