I have video viewing data (length of session, nb of videos, etc), as well as if the user clicked on the like button. We can use the like button as a confirmation that the user had a positive viewing experience, however, only 0.1% of users click on this button. I would like to find a way to find users that have a similar data to those who liked the video without having them explicitly click the like button.
I thought about having the like variable be the response variable in a binary classification problem, however, not liking the video does not mean negative experience.
I also thought of maybe treating it as an unsupervised task, where I look if the liked sessions fall naturally inside a specific cluster.
Edit: I did not make it clear, but the service is similar to Youtube, where we are trying to figure out if a user had a positive viewing experience after clicking on a video. Right now, there is no recommendation engine and this is the first part in building one.
Edit: After the answers, I am leaning more towards approaching this task as an unsupervised learning task, rather than supervised.
Any thoughts how to approach this problem? Thanks