Vectorization of transactions

A processing system, comprising:

• a memory comprising computer-executable instructions;
• a processor configured to execute the computer-executable instructions and cause the processing system to:
• receive electronic transaction information of one or more transactions of a user;
• for each transaction of the one or more transactions:
• segment, based on a segmenting criteria, the electronic transaction information of the transaction into one or more transaction words;
• generate, based on the one or more transaction words, a second transaction description related to the transaction;
• and
• identify, based on the second transaction description, a category of the transaction;
• generate, based on the corresponding identified categories of the one or more transactions, a set of transaction history data of the user;
• provide the set of transaction history data of the user as an input to a machine learned model trained to output a set of word embedding vectors based on the set of transaction history data;
• determine, based on an output of the machine learned model comprising a set of word embedding vectors, a set of similar merchants;
• and
• provide the set of similar merchants for display to the user.

A non-transitory computer-readable medium comprising computer-executable instructions, which, when executed by a processing system, cause the processing system to perform a method, the method comprising:

• receiving electronic transaction information of one or more transactions of a user;
• for each transaction of the one or more transactions:
• segmenting, based on a segmenting criteria, the electronic transaction information of the transaction into one or more transaction words;
• generating, based on the one or more transaction words, a second transaction description related to the transaction;
• and
• identifying, based on the second transaction description, a category of the transaction;
• generating, based on the corresponding identified categories of the one or more transactions, a set of transaction history data of the user;
• providing the set of transaction history data of the user as an input to a machine learned model trained to output a set of word embedding vectors based on the set of transaction history data;
• determining, based on an output of the machine learned model comprising a set of word embedding vectors, a set of similar merchants;
• and
• providing the set of similar merchants for display to the user.