3. The method of any one of embodiments 1-2, further comprising: receiving a second user action during the conversational interaction with the user interface; in response to receiving the second user action, determining a second feature input for the first machine learning model based on the second user action; inputting the second feature input into the first machine learning model; receiving a different output from the first machine learning model, wherein the different output corresponds to a different intent cluster from the plurality of intent clusters; and inputting the different output into the second machine learning model.
4. The method of any one of embodiments 1-3, wherein the first machine learning model is a supervised machine learning model, and wherein the second machine learning model is a supervised machine learning model.
5. The method of any one of embodiments 1-4, wherein the first machine learning model is a factorization machine model, and wherein the second machine learning model is an artificial neural network model.
6. The method of any one of embodiments 1-5, further comprising clustering available specific intents into the plurality of intent clusters.
7. The method of any one of embodiments 1-6, further comprising: receiving a first labeled feature input, wherein the first labeled feature input is labeled with a known intent cluster for the first labeled feature input; and training the first machine learning model to classify the first labeled feature input with the known intent cluster.
8. The method of any one of embodiments 1-7, wherein the first feature input is a conversational detail or information from a user account of the user.
9. The method of any one of embodiments 1-8, wherein the first feature input indicates a time at which the user interface was launched.
10. The method of any one of embodiments 1-9, wherein the first feature input indicates a webpage from which the user interface was launched.