Using a recurrent neural network and a classification machine learning model to predict actions in software applications

A system for machine learning based action prediction, comprising:

• one or more processors;
• and
• a memory storing instructions that, when executed by the one or more processors, cause the system to:
• provide, as inputs to a recurrent neural network (RNN), an ordered sequence of strings representing actions performed by a user within a software application, the RNN having been trained through a supervised learning process to:
• generate embeddings of the ordered sequence of strings;
• and
• generate a numerical score relating to a target action based on the embeddings and an order of the ordered sequence of strings;
• receive, as an output from the RNN in response to the inputs, the numerical score relating to the target action;
• provide, as respective inputs to a tree-based classification machine learning model, the numerical score and an additional feature relating to the user;
• receive, as a respective output from the tree-based classification machine learning model in response to the respective inputs, a propensity score indicating a likelihood of the user to perform the target action;
• and
• perform an action within the software application based on the propensity score.

A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to:

• provide, as inputs to a recurrent neural network (RNN), an ordered sequence of strings representing actions performed by a user within a software application, the RNN having been trained through a supervised learning process to:
• generate embeddings of the ordered sequence of strings;
• and
• generate a numerical score relating to a target action based on the embeddings and an order of the ordered sequence of strings;
• receive, as an output from the RNN in response to the inputs, the numerical score relating to the target action;
• provide, as respective inputs to a tree-based classification machine learning model, the numerical score and an additional feature relating to the user;
• receive, as a respective output from the tree-based classification machine learning model in response to the respective inputs, a propensity score indicating a likelihood of the user to perform the target action;
• and
• perform an action within the software application based on the propensity score.