Systems and methods for machine learning-based targeted link sharing

A computer-implemented method for targeted link sharing, comprising:

• detecting a link for sharing on a computing device associated with a sharing user;
• identifying criteria for using a code associated with the link;
• determining one or more users from a plurality of users associated with the sharing user that are suitable to use the link based on the criteria and a plurality of interaction histories of the plurality of users;
• for each of the one or more users:
• providing an interaction history of the respective user, a code use behavior of the respective user, and information associated with the code as inputs to a trained machine learning system;
• receiving a likelihood that the respective user uses the code as output of the trained machine learning system;
• and
• determining whether the output meets a predefined criterion;
• generating a list of receiving users that includes each of the one or more users determined to have the output meet the predefined criterion;
• providing a notification including the list for display on the computing device associated with the sharing user;
• receiving a selection of at least one of the one or more receiving users from the list to share the link with;
• distributing the link to at least one computing device associated with the at least one of the one or more receiving users;
• and
• causing the at least one computing device to dynamically display a notification including at least one of:
• the link on a web browser executing on the at least one computing device in response to detecting navigation to a site associated with a merchant providing the link on the web browser, or the link as an embedding within search results of a search engine executing on the at least one computing device in response to detecting the search results include a site associated with a merchant providing the link.

A method for training a machine learning system to predict a likelihood of code use, the method comprising:

• receiving a plurality of training datasets associated with a plurality of users, each of the plurality of training datasets including an interaction history and a code use behavior of a respective user of the plurality of users, information associated with a past code received by the respective user, and an indication of whether the past code was used;
• providing at least a portion of the plurality of training datasets as input to a machine learning system to train the machine learning system to predict a user-specific likelihood of code use, wherein responsive to detecting a link including a current code for sharing on a computing device associated with a sharing user, the trained machine learning system is deployed to predict a likelihood of use of the current code for each of one or more users that are associated with the sharing user and are determined to be suitable to use the code;
• receiving feedback associated with at least one of the one or more users that the sharing user selected to share the current code with, the feedback indicating whether the current code was used;
• and
• updating the trained machine learning system based on the feedback.