Clustering based entity matching using multiple queues

A non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors cause the one or more computer processors to:

• receive information identifying a plurality of entities comprising source entities and target entities, wherein an entity is associated with one or more resources;
• for each of the plurality of entities, generate a vector representation;
• cluster target entities to generate a plurality of clusters based on vector distances between pairs of target entities, each cluster comprising target entities that are close to other target entities within the cluster compared to target entities of remaining clusters;
• and
• optimize an aggregate score across the plurality of entities using a plurality of queues, each queue associated with a source entity and including one or more target entities, wherein instructions for optimizing cause the one or more computer processors to:
• identify a cluster of target entities based on a distance between the source entities and entities of the cluster of target entities,
• select a subset of target entities from the identified cluster using a machine learning based model trained to determine a score between an input source entity and target entity;
• and
• modify one or more queues from the plurality of queues to optimize the aggregate score.

A computer system comprising:

• one or more computer processors;
• and
• a non-transitory computer readable storage medium storing instructions that when executed by one or more computer processors cause the one or more computer processors to:
• receive information identifying a plurality of entities comprising source entities and target entities, wherein an entity is associated with one or more resources;
• for each of the plurality of entities, generate a vector representation;
• cluster target entities to generate a plurality of clusters based on vector distances between pairs of target entities, each cluster comprising target entities that are close to other target entities within the cluster compared to target entities of remaining clusters;
• and
• optimize an aggregate score across the plurality of entities using a plurality of queues, each queue associated with a source entity and including one or more target entities, wherein instructions for optimizing cause the one or more computer processors to:
• identify a cluster of target entities based on a distance between the source entities and entities of the cluster of target entities,
• select a subset of target entities from the identified cluster using a machine learning based model trained to determine a score between an input source entity and target entity;
• and
• modify one or more queues from the plurality of queues to optimize the aggregate score.