Resolving and merging duplicate records using machine learning

A computer-implemented method for resolving duplicate records using machine learning, comprising:

• receiving a plurality of records previously identified as being duplicate records representing the same entity, wherein at least a subset of the duplicate records comprise conflicting data for the entity, each duplicate record comprising values for a plurality of data fields;
• at a processor, generating a plurality of feature vectors, each feature vector comprising a plurality of features describing characteristics of one of the records;
• applying at least one machine learning model to the feature vectors to generate scores for the feature vectors;
• and
• for each of at least a subset of the data fields:
• displaying, at an output device, a plurality of values, each value corresponding to at least one of the duplicate records;
• and
• for each displayed value, displaying, at the output device, a score for a feature vector generated using the displayed value.

A computer program product for resolving duplicate records using machine learning, comprising:

• a non-transitory computer-readable storage medium;
• and
• computer program code, encoded on the medium, configured to cause at least one processor to perform the steps of:
• receiving a plurality of records previously identified as being duplicate records representing the same entity, wherein at least a subset of the duplicate records comprise conflicting data for the entity;
• generating a plurality of feature vectors, each feature vector comprising a plurality of features describing characteristics of one of the records;
• applying at least one machine learning model to the feature vectors to generate at least one resolved record by resolving the conflicting data;
• and
• causing an output device to output the at least one resolved record.

A system for resolving duplicate records using machine learning, comprising:

• a processor, configured to:
• receive a plurality of records previously identified as being duplicate records representing the same entity, wherein at least a subset of the duplicate records comprise conflicting data for the entity;
• generate a plurality of feature vectors, each feature vector comprising a plurality of features describing characteristics of one of the records;
• and
• apply at least one machine learning model to the feature vectors to generate at least one resolved record by resolving the conflicting data;
• and
• an output device, communicatively coupled to the processor, configured to output the at least one resolved record.