| CPC G06Q 30/0252 (2013.01) [G06Q 30/0256 (2013.01); G06Q 30/0277 (2013.01)] | 20 Claims |
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1. A system, comprising:
a processor; and
a non-transitory memory storing instructions that, when executed, cause the processor to:
based on a first frequency, generate a plurality of seasonal time windows each of which represents a time period associated with a respective seasonal event, wherein the plurality of seasonal time windows are generated by:
initializing, within a database, a set of cluster centroids based on at least one of the plurality of seasonal time windows and a search query; and
executing an iterative clustering algorithm that operates on each of the set of cluster centroids and converges each one of the set of cluster centroids to find a start bound and an end bound for each of a set of time bounds for each of the at least one of the plurality of seasonal time windows;
store the at least one of the plurality of seasonal time windows in thea database;
for each of the plurality of seasonal time windows, based on a second frequency, wherein the second frequency is greater than the first frequency:
obtain from the database catalog data for a plurality of products;
based on the catalog data, determine a plurality of seasonal product types that corresponds to a different seasonal time window of the at least one of the plurality of seasonal time windows;
obtain from the database historical transaction data, from a plurality of user transaction data stored in the database, of the seasonal product types;
execute an algorithm to operate on the corresponding seasonal time window, each item associated with the seasonal product types and the historical transaction data, and generate a first seasonal index score for each item in the historical transaction data;
execute the algorithm to operate on the corresponding seasonal time window, the seasonal product types, and the historical transaction data, and generate, for each product type in the historical transaction data, a second seasonal index score, wherein each product type includes one or more items in the historical transaction data;
generate a seasonal rank score for each item in the historical transaction data based on the first seasonal index score and the second seasonal index score, and store the seasonal rank score in the database;
generate a ranked item set of the one or more items in the historical transaction data based on their respective seasonal rank scores; and
store the ranked item set in the database, where in the ranked item set is associated with the corresponding seasonal time window;
during a current user session, receive a transaction order from a user device of a user, and store the transaction order in the database;
responsive to receiving the transaction order:
determine a current seasonal time window based on the plurality of seasonal time windows and a current time;
extract, from the transaction order, a value of items in the transaction order;
determine a consideration intent of the user based on the value of items in the transaction order, the consideration intent identifying one of a plurality of intent groups;
select at least a portion of the ranked item set associated with the current seasonal time window as a final list of recommended items, personalized to the user, based on the transaction order and the consideration intent; and
transmit, to the web server in an acceptable data format, instructions that cause inclusion of one or more items from the final list of recommended items for display on at least one post transaction webpage to the user device;
during the current user session, receive an updated transaction order from the web server for the user device of the user, the updated transaction order comprising a subset of recommended items selected by the user from the final list of recommended items displayed to the user; and
in response to receiving the updated transaction order, update the transaction order within the database by adding the subset of recommended items into the transaction order, wherein the transaction order is executed to purchase the subset of recommended items.
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