Incorporating user feedback in a ML model

Question

I have developed a ML model for a classification (0/1) NLP task and deployed it in production environment. The prediction of the model is displayed to users, and the users have

Answer 1

Build a simple, light model(s) that can be updated per feedback. Online Machine learning gives a number of candidates for this

Most good online classifiers are linear. In which case we can have a couple of them and achieve non-linearity by combining them via a small shallow neural net

https://stats.stackexchange.com/questions/126546/nonlinear-dynamic-online-classification-looking-for-an-algorithm

Answer 2

There could be couple of ways to do this:

1) You can incorporate the feedback that you get from the user to only train the last layer of your model, keeping the weights of all other layers intact. Intuitively, for example, in case of CNN this means you are extracting the features using your model but slightly adjusting the classifier to account for the peculiarities of your specific user.

2) Another way could be to have a global model ( which was trained on your large training set) and a simple logistic regression which is user specific. For final predictions, you can combine the results of the two predictions. See this paper by google on how they do it for their priority inbox.