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 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 functions comprising: generating one or more feature vectors for a user, the one or more feature vectors at least comprising transaction-based features and slot-based features; generating, using a machine learning architecture, a repurchase prediction for the user based, at least in part, on the one or more feature vectors; generating, using the machine learning architecture, a time slot prediction for the user based, at least in part, on the one or more feature vectors, the time slot prediction predicting a time slot desired by the user for an upcoming transaction; and executing a reservation function that facilitates reserving of the time slot for the user. 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, perform certain acts. The acts can include obtaining reorder likelihood scores for items that a user has ordered historically. The acts also can include grouping the items into groups using a taxonomy. The acts additionally can include adjusting the groups based on a respective number of items in each of the groups and a respective group-specific threshold for each of the groups. The acts further can include ranking the items within the groups based on the reorder likelihood scores. Other embodiments are described.

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 functions comprising: receiving in-session user activity comprising types of user interactions during a browsing session displayed by a graphical user interface (GUI) of an electronic device of a user; generating a distribution of interaction counts of interactions with the GUI over a period of time; predicting, using a set of predictive algorithms, one or more intents of the user based on the distribution of interaction counts; and transmitting instructions to display an altered GUI on the electronic device of the user based on the one or more intents of the user, as predicted. Other embodiments are disclosed herein.

Abstract: This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

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 method including tracking impression response data in response to online impressions of content elements displayed to users of a website. The method also can include performing a classification of the users based on the impression response data. The method additionally can include generating a webpage of the website to comprise a content element selected from among the content elements based on the classification of a user of the users. 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 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.

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: This application relates to apparatus and methods for automatically determining and providing personalized user personas of a customer for specific platforms (e.g., applications). In some examples, a computing device receives a persona request identifying a user and a platform. In response, the computing device obtains user data associated with the user and a plurality of potential user personas from a database. For each of the plurality of potential user personas, the computing device then determines a combination score for the user based on the user data. The combination score indicates user's affinity to a corresponding potential user persona within the platform. The computing device selects at least one potential user persona of the plurality of potential user personas as a final user persona for the user and the platform based on the corresponding combination score.

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 processors and cause the one or more processors to perform functions comprising determining one or more similar items similar to an item; determining one or more complementary items complementary to both the one or more similar items and the item; applying one or more labels to the one or more complementary items based on a rank of the one or more complementary items; training a predictive algorithm on the one or more labels; receiving a request to generate a customized graphical user interface (GUI) for the item; and coordinating displaying the customized GUI for the item using the predictive algorithm. 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: 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 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: 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 personalized recommendation system can include a computing device configured to receive an indication of a selection from a user device identifying an item, a user, and a third-party. The computing device is configured to obtain historical data for the user and third-party data for the third-party, generate a user representation based on the historical data, and identify a set of items associated with the third-party based on the item. The computing device is configured to obtain attributes for each item and, for each item of the set of items, determine a corresponding ranking based on the third-party data, the user representation, and attributes for the corresponding item. The computing device is configured to organize a display of the set of items based on the corresponding ranking of each item and transmit the organized display of the set of items to the user device for display on a user interface.

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 method including tracking impression response data in response to online impressions of content elements displayed to users of a website. The impression response data can include (i) first responses by the users in one or more physical stores in response to the online impressions, and (ii) second responses by the users in the website in response to the online impressions. The method also can include receiving a request from a user of the users to display a webpage of the website. The method additionally can include generating the webpage to include a content element selected from among the content elements based on a classification of the user and the impression response data for the content elements. Other embodiments of related systems and methods are described.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when executed to run on the one or more processors, cause the one or more processors to perform: receiving in-session user activity entered into on a graphical user interface (GUI) from a user electronic device of a user; generating, using a predictive algorithm, a ranked list of one or more likely intents of the user to perform one or more actions on the GUI; 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; and coordinating a display of a likely-to-be-used GUI element on the GUI. Other embodiments are disclosed herein.