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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!

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  • $\begingroup$ IMO user_id and video_id are not good features to predict watch time. Instead properties of user and video should be used. Eg user A likes animals and nature and video B is about flowers (most probably high watch time). Else user_A, video_B simply does not capture such information $\endgroup$
    – Nikos M.
    Jun 19 at 12:41
  • $\begingroup$ @NikosM. I'm using a collaborative Filter wherein the values of my data are video watch duration instead of ratings (1-5) $\endgroup$
    – KMLearner
    Jun 19 at 14:17
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No - using a recommender system to predict watch duration is not going to work well.

Watch duration is a continuous target so it would be more useful to model your scenario as a regression problem.

Try to the find the user and video features that are associated with differences in watch duration time.

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