Methods and apparatus for electronically providing item advertisement recommendations

A computer-implemented method comprising:

• obtaining, by a processor of a server, over one or more communication networks, and from a database storing transaction data of each customer of a plurality of customers based on data obtained from a computing device of each of the plurality of customers of an online platform, the transaction data characterizing a plurality of purchases and a category associated with each of the plurality of purchases;
• obtain, by the processor of the server, over one or more communication networks, and from a second server, online session data of the plurality of customers;
• for each customer of the plurality of customers, obtaining a plurality of item advertisement recommendations based on transaction data of the customer;
• training, by the processor of the server, a machine learning process with a first portion of the online session data and a second portion of the transaction data and implementing, by the processor of the server and by applying the trained machine learning process to at least the transaction data, a first set of operations that generate, for each customer of the plurality of customers, mask data, the first set of operations including:
• generating, by the processor of the server and for each customer of the plurality of customers, first data identifying (i) a plurality of categories associated with the plurality of purchases, and (ii) for each of the plurality of categories, a corresponding frequency of purchases, based on the second portion of the transaction data associated with each customer of the plurality of customers;
• generating, by the processor of the server and for each customer, a plurality of relevancy scores for pairs of the plurality of categories based on the first data;
• generating, by the processor of the server and for each customer, the mask data identifying a subset of categories from the plurality of categories based on the plurality of relevancy scores;
• for each customer and based on the mask data of each customer, implementing, by the processor of the server, a set of item advertisement recommendation operations, the one or more item advertisement recommendation operations including:
• for a corresponding plurality of item advertisement recommendations, determining an associated item category;
• for the corresponding plurality of item advertisement recommendations, determining whether the associated item category matches at least one of the categories of the subset of categories by comparing the associated item category to at least the portion of the corresponding mask data;
• selecting each of the obtained plurality of item advertisement recommendations that are determined to have the associated item category that matches at least one of the categories of the subset of categories;
• and
• in response to a corresponding computing device of the customer accessing the online platform, causing, by communicating with a second server, over the one or more communication networks, that hosts an online platform that is accessible by a computing device of each of the plurality of customers of the online platform, the online platform displayed on a corresponding computing device of the customer to include at least one of the selected item advertisement recommendations based on data indicating the selected one or more item advertisement recommendations.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:

• obtaining, over one or more communication networks and from a database storing transaction data of each customer of a plurality of customers based on data obtained from a computing device of each of the plurality of customers of an online platform, the transaction data characterizing a plurality of purchases and a category associated with each of the plurality of purchases;
• obtaining, over one or more communication networks and from a server, online session data of the plurality of customers;
• for each customer of the plurality of customers, obtaining a plurality of item advertisement recommendations based on transaction data of the customer;
• training a machine learning process with a first portion of the online session data and a first portion of the transaction data and implementing, by applying the trained machine learning process to at least a second portion of the transaction data, a first set of operations that generate, for each customer of the plurality of customers, mask data, the first set of operations including:
• generating, for each customer of the plurality of customers, first data identifying (i) a plurality of categories associated with the plurality of purchases, and (ii) for each of the plurality of categories, a corresponding frequency of purchases, based on the second portion of the transaction data associated with each customer of the plurality of customers;
• generating, for each customer, a plurality of relevancy scores for pairs of the plurality of categories based on the first data;
• generating, for each customer, the mask data identifying a subset of categories from the plurality of categories based on the plurality of relevancy scores;
• for each customer and based on the mask data of each customer, implementing a set of item advertisement recommendation operations, the set of item advertisement recommendation operations including:
• for a corresponding plurality of item advertisement recommendations, determining an associated item category;
• for the corresponding plurality of item advertisement recommendations, determining whether the associated item category matches at least one of the categories of the subset of categories by comparing the associated item category to at least the portion of the corresponding mask data;
• selecting each of the obtained plurality of item advertisement recommendations that are determined to have the associated item category that matches at least one of the categories of the subset of categories;
• and
• in response to a corresponding computing device of the customer accessing the online platform, causing, by communicating, over the one or more communication networks, with a second server that hosts an online platform that is accessible by a computing device of each of the plurality of customers of the online platform, the online platform displayed on a corresponding computing device of the customer to include at least one of the selected item advertisement recommendations based on data indicating the selected one or more item advertisement recommendations.