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 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: 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.

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: 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: 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 an initial graphical user interface (GUI) from a user electronic device of a user; selectively aggregating the in-session user activity of the user with historical activity data of the user; predicting one or more intents of the user by inputting the in-session user activity of the user and the historical activity data of the user into a first set of predictive algorithms; post-processing the one or more intents; and coordinating displaying an altered GUI based on the one or more intents, as filtered. 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: 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: 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 an initial graphical user interface (GUI) from a user electronic device of a user; selectively aggregating the in-session user activity of the user with historical activity data of the user; predicting one or more intents of the user by inputting the in-session user activity of the user and the historical activity data of the user into a first set of predictive algorithms; post-processing the one or more intents; and coordinating displaying an altered GUI based on the one or more intents, as filtered. Other embodiments are disclosed herein.

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: 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.