I just want to ask If I can use Surprise Library (SVD algorithm) in building a recommender system that predicts the watch duration for a user_id and video_id pair?
I have a dataset that contains the user_id, video_id, and watch_duration of the user_id to the video_id.
The watch duration ranges from 1s to 1014573s and is very right skewed (Most watch duration are 1-3000s) and I'm just wondering If I can use the surprise package in making an SVD model that predicts the watch_duration and not a traditional rating system (1-5)?
I tried this method and got a 3500 RMSE which I think is really bad. I got the same RMSE from my CV result and my hold out set so the model doesn't overfit, the results are just really bed.
Any help will be appreciated, thanks!