Heterogeneous graph embedding

A computing system, comprising:

• an application server, the application server comprising a processor and a memory;
• and
• a database in communication with the processor and configured to store a plurality of transactions between users and merchants, the memory storing computer-executable instructions which are executed by the processor to cause the application server to:
• receive from the database, a plurality of feature datasets, each feature dataset being associated with a transaction corresponding to a user identifier and a merchant identifier;
• process, based on a transaction characteristic and a merchant category, the feature datasets to generate a graph data structure comprising a plurality of interconnected transaction pairs;
• process the graph data structure to determine a first-order transaction pair corresponding to a maximum transaction frequency associated with a first user identifier, the first-order transaction pair comprising a first user node associated with the first user identifier and a first merchant node associated with a first merchant;
• process the graph data structure to determine a second set of transaction pairs from the first merchant node to one or more additional user nodes based on a ranking of transaction frequencies associated with the first merchant node to the one or more additional user nodes;
• determine a second-order transaction pair from the first merchant node to a second user node based on each transaction frequency of the second set of transaction pairs and a corresponding predefined hyper-parameter;
• and
• generate a transaction sequence associated with the first user identifier by combining respective feature datasets associated with the first-order transaction pair and the second-order transaction pair.

A method implemented by a server computing device, the server computing device comprising a processor and a memory, the memory storing executable instructions that when executed by the processor cause the server computing device to perform processing comprising:

• receiving, from a database in communication with the processor, a plurality of feature datasets, each feature dataset being associated with a transaction corresponding to a user identifier and a merchant identifier;
• processing, based on a transaction characteristic and a merchant category, the feature datasets to generate a graph data structure comprising a plurality of interconnected transaction pairs;
• processing the graph data structure to determine a first-order transaction pair corresponding to a maximum transaction frequency associated with a first user identifier, the first-order transaction pair comprising a first user node associated with the first user identifier and a first merchant node associated with a first merchant;
• processing the graph data structure to determine a second set of transaction pairs from the first merchant node to one or more additional user nodes based on a ranking of transaction frequencies associated with the first merchant node to the one or more additional user nodes;
• determining a second-order transaction pair from the first merchant node to a second user node based on each transaction frequency of the second set of transaction pairs and a predefined hyper-parameter associated with a corresponding payment method;
• generating a transaction sequence associated with the first user identifier by combining respective feature datasets associated with the first-order transaction pair and the second-order transaction pair;
• executing an embedding model to transform the transaction sequence into a feature embedding;
• and
• applying a machine learning model to the feature embedding to generate corresponding feature prediction and classification.