Abstract: Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.

Abstract: Systems and methods for matching entities to target objects using an ensemble model are disclosed. The ensemble model includes a general trained machine learning (ML) model (which is trained using the entirety of a training dataset) and a subarea trained ML model (which is trained using a subset of the training dataset corresponding to a specific, defined subarea) that provides potential matches to a meta-model of the ensemble model to generate a final match. The ensemble model may also include a general trained natural language processing (NLP) model and a subarea trained NLP model that provides potential matches to the meta-model. The meta-model of a quad-ensemble ML model combines the four potential matches (such as probabilities and similarities of matching specific pairs of targets objects and entities) to generate a final match (such as a final probability used to identify the final match).

Abstract: Systems and methods for optimally formatting item identifiers (ID) are disclosed. An example method is performed by one or more processors of a system and includes obtaining descriptions of items, identifying, for each item, one or more attributes of the item described in the item's description, extracting a value for each of the identified attributes, identifying a set of common attributes among the identified attributes for which values were extracted for more than a threshold ratio of the items, assigning a priority weight to each of the common attributes using an optimization algorithm, identifying a set of optimum attributes among the set of common attributes based on the priority weights, mapping an optimum code to each unique value extracted for the optimum attributes, and generating an optimum ID format that provides, for each item, a unique ID including the optimum codes mapped to the values of the item's optimum attributes.