I am working on a supervised learning problem for a web-search task, where I have access to a relatively small set of human-labeled examples and lots of user-behavior data.
Now, user behavior data is biased, because of presentation bias, position bias etc. So it’s likely that its’ distribution will be different from human-labeled data.
I am planning to use both to train a Neural Network model.
Now I am confused about how to combine both datasets?