Abstract: A system including one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using a first machine learning model, a first output comprising a repurchase prediction for a user; generating, using a second machine learning model and using respective data of the repurchase prediction of the first machine learning model for the user, a second output comprising a time slot prediction for the user; initiating one or more reservation functions based at least in part on the first output and the second output; and transmitting an option to the user to access a GUI of a digital shopping cart system to reserve a reservation function of the one or more reservation functions. Other embodiments are described.

Abstract: System and method for time-aware deep learning are provided. A training data set including continuous-time data and a deep learning architecture are received. A trained deep learning model is generated using a temporal kernel approach. The temporal kernel approach includes constructing a temporal kernel based on the continuous-time data and composing the temporal kernel with a selected hidden layer of the deep learning architecture to generate a hidden output. The trained deep learning model is output for use in one or more machine learning tasks.

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: outputting, by a machine-learning model, a probability that a user will re-order two or more items at a present time; determining the two or more items to recommend to the user based on the probability exceeding a predetermined threshold that the user will re-order the two or more items at the present time; sending instructions to display the two or more items to the user, wherein the user interface comprises a single-click option to add to an electronic cart the two or more items; and after receiving the single-click option from the user interface, adding the two or more items to the electronic cart. Other embodiments are disclosed.

Abstract: A computer-implemented method including automatically generating predictions of a respective number of items that a user is likely to reorder in each of groups of the items that a user has ordered historically. The method also can include ranking the groups based on the predictions of the respective number of the items the user is likely to reorder in each of the groups. The method additionally can include transmitting for display to the user a user interface including the groups of the items. Other embodiments are described.

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: 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: mapping each item of multiple items in a mixed-intent basket to a respective product type code (PT code); generating a respective list of complementary product type codes from each respective PT code; generating, using a complementary item algorithm, a respective candidate set of complementary items; detecting a platform-level configuration of a platform used by an electronic device of a user; loading, using diversity rotation, the respective quantity of complementary items onto a website carousel; and displaying the website carousel, as loaded, on the electronic device of the user, wherein the website carousel is sized to fit the platform-level configuration. Other embodiments are disclosed.

Abstract: Systems and methods 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: receiving a user request via a graphical user interface, the user request corresponding to a user search query for a product; determining whether a first processing machine of the system is operating in a first processing mode or a second processing mode; when the first processing machine is determined to be operating in the first processing mode, analyzing the user request via the first processing machine and using a process, to identify a candidate recommendation system to utilize by: determining a randomized strategy for one or more candidate recommendation systems based on a ratio of a number of the one or more candidate recommender systems, the randomized strategy to be stored in a collected history data; determining model parameters based on the collected history data; and

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 in-session user activity entered into on an initial graphical user interface (GUI) from a user electronic device of a user; pre-processing the in-session user activity to determine one or more intents of the in-session user activity; comparing the one or more intents of the in-session user activity with one or more complementary intents; and coordinating displaying a complimentary GUI on the user device of the user based on the one or more complementary intents. Other embodiments are disclosed herein.

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 functions comprising: receiving one or more vectors representing one or more types of features for a pair of items; generating, using a similarity item model of a machine learning architecture, a prediction for a similar item, wherein the similarity item model combines a pair of separately trained machine learning models; combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items; and transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the website. Other embodiments are disclosed.

Abstract: Example implementations relate to generating keywords. An item data structure including textual information is received. A first context associated with the textual information is determined and a plurality of keywords is generated using a first trained model that receives the textual information and the first context. A plurality of matching item data structures including respective textual information corresponding to a plurality of matching items associated with the item is received. A set of reference keywords is generated using a second trained model that receives the respective textual information and one or more of second contexts. A relevancy score is determined between at least one keyword and the first context using the first trained model, and an interface that includes the at least one keyword is generated.

Abstract: Systems and methods of attribute extraction and labelling are disclosed. An input dataset is received and a plurality of preliminary attribute labels are generated for at least a first attribute of a first element in the input dataset. Each preliminary attribute label in the plurality of preliminary attribute labels is generated by one of a plurality of large language models (LLM). A final attribute label for the first attribute is generated based on a weighted combination of the plurality of preliminary attribute labels for the first attribute and a data structure representative of the first element is updated to include the final attribute label for the first attribute.

Abstract: This application is directed to systems and methods for cross-category item recommendation or ranking. In some embodiments, a disclosed method includes receiving interaction data indicative of an interaction with an information item associated with an anchor item in a first category; in accordance with a determination that the first category is associated with a plurality of themes of a second category, applying at least one type selection model to determine a set of item types associated with the plurality of themes of the second category; generating an ordered list of recommended items of the second category based on the set of item types; and in response to the interaction data, enabling display of the ordered list of recommended items of the second category on a display of a client device. In some embodiments, a large language model is applied to determine the plurality of themes of the second category.

Abstract: In various embodiments, systems and methods for generating interfaces including similar elements are disclosed. An interface request identifying an anchor element is received and a set of similar elements for the anchor element identifier is generated by implementing an inference recommendation model generated by a Siamese wide and deep training framework. The inference recommendation model is configured to receive at least one recall set of candidate elements and generate a similarity score for each candidate element in the set of candidate elements and the anchor element. An interface including at least one similar element selected from the set of similar elements is generated and transmitted to a user device associated with the interface request.

Abstract: Systems and methods for providing item recommendations based on item images or uploaded images are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining an anchor image based on the recommendation request; generating at least one query based on the anchor image; generating, using a language model, textual recommendation data based on the at least one query; generating, using at least one machine learning model, at least one ranked list of recommended items based on the textual recommendation data; and transmitting to the computing device the at least one ranked list of recommended items to be displayed to the customer.

Abstract: Systems and methods for generating an interface including recommended elements selected using generated element type relation labels are disclosed. An interface generation request including at least one element type is received and a set of recommended elements is generated based on element type relations between the at least one element type and additional element types associated with a network interface. The element type relations are generated by at least one large language model and at least one optimal relation generation prompt. An interface including the set of recommended elements is generated.

Abstract: System and methods for generating cohesive product recommendations are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical customer data associated with a customer, receiving an indication of a customer's selection of a first product, parsing and extracting first product description data from catalog description data, generating summary data of the first product description data, the summary data being a subset of the first product description data, and generating a plurality of recommended products based on the summary data, the historical customer data, and at least one business rule.

Abstract: Systems and methods for providing item recommendations based on dual models with different levels of product data granularity are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request for recommending items to a customer; determining, based on the recommendation request, at least one anchor item to be displayed to the customer; obtaining a first machine learning model trained based on a first product data granularity; obtaining a second machine learning model trained based on a second product data granularity; generating, using the first machine learning model and the second machine learning model, a ranked list of recommended items based on the at least one anchor item; and transmitting to the computing device the ranked list of recommended items to be displayed to the customer with the at least one anchor item.

Abstract: This application is directed to systems and methods for information recommendation. In some embodiments, a disclosed method includes obtaining historic interaction data associated with past interactions of a plurality of users with a plurality of information items; generating a first set of item types involved in the past interactions jointly with an anchor item type; generating a second set of item types semantically associated with the anchor item type, e.g., for a user class; combining the first set of item types and the second set of item types to generate a list of target item types; generating a list of recommended information items based on the list of target item types; and in response to a first user's interaction with the anchor item type, enabling display of at least a subset of information items in the list of recommended information items on an electronic device associated with the first user.

Abstract: A system can include 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: selectively aggregating in-session user activity of a user with historical activity data of the user into one or more respective groups based on interactions of the user with a GUI over a period of time; predicting, using a set of predictive algorithms, one or more intents of the user based on the one or more respective groups; and facilitating a display of an altered GUI on an electronic device of the user based on the one or more intents of the user, as predicted. 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: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.