Probabilistic methods and systems for resolving anonymous user identities based on artificial intelligence

An identity resolution system for probabilistically resolving user identities, the identity resolution system comprising:

• a communication interface;
• a memory comprising executable instructions;
• and
• a processor communicably coupled to the communication interface, the processor configured to execute the executable instructions to cause the identity resolution system to at least:
• receive user interaction data associated with a user of a business interface, wherein the user interaction data is aggregated from one or more sources associated with the business interface;
• extract a plurality of user attributes associated with the user from the user interaction data;
• determine one or more candidate user profiles associated with a predefined location among a plurality of user profiles, the predefined location based at least on a location attribute of the plurality of user attributes;
• predict a likelihood of the user interaction data to be associated with at least one candidate user profile by performing steps:
• applying one or more machine learning models on each of the one or more candidate user profiles to determine a matching probability score with the user interaction data, wherein the matching probability score is determined by mapping each attribute of the plurality of user attributes to a corresponding attribute in each of the one or more candidate user profiles;
• and
• identifying the at least one candidate user profile associated with the matching probability score greater than a predefined threshold;
• merge the plurality of user attributes from the user interaction data with a plurality of user attributes associated with the at least one candidate user profile for generating a user profile;
• and
• assign a user identifier for the user profile. The identity resolution system as claimed in claim 9, wherein for applying the one or more machine learning models, the identity resolution system is caused to at least:
• calculate a temporal similarity measure between the user interaction data and each of the one or more candidate user profiles based at least in part on mapping of a temporal data of the user interaction data with a corresponding temporal data associated with each of the candidate user profiles.

A computer-implemented method for probabilistically resolving user identities, the computer-implemented method comprising:

• receiving, by a processor, user interaction data associated with a user of a business interface, wherein the user interaction data is aggregated from one or more sources associated with the business interface;
• extracting, by the processor, a plurality of user attributes associated with the user from the user interaction data based on a flexible metadata based mapping;
• determining, by the processor, one or more candidate user profiles associated with a predefined location among a plurality of user profiles, the predefined location based at least on a location attribute of the plurality of user attributes;
• predicting, by the processor, a likelihood of the user interaction data to be associated with at least one candidate user profile by performing steps:
• applying, by the processor, one or more machine learning models on each of the one or more candidate user profiles to determine a matching probability score with the user interaction data, wherein the matching probability score is determined by mapping each user attribute of the plurality of user attributes to a corresponding user attribute in each of the one or more candidate user profiles;
• and
• identifying, by the processor, the at least one candidate user profile associated with the matching probability score greater than a predefined threshold;
• merging, by the processor, the plurality of user attributes from the user interaction data with a plurality of user attributes associated with the at least one candidate user profile for generating a user profile;
• creating, by the processor, an identity graph for the user profile based at least in part on the plurality of user attributes from the user interaction data and the plurality of user attributes associated with the at least one candidate user profile;
• and
• assigning, by the processor, a user identifier for the user profile.