I am trying to estimate how much a user liked a video using how much of the video they watched. Let's say, on the scale of 1 to 10, 1 means that the user didn't like it at all, and 10 means they enjoyed it a lot. For instance, if a user watched 8 minutes from a 10-minute video, it means the score of 8. If they watch 18 minutes of a 20-minute video, it means the score of 9.

The problem is, the probability of a short video (say 1 minute) being completely watched is much higher than that of a long video (say 120 minutes). It doesn't necessarily mean that the user liked it more. It just was short.

I am looking for an equation to consider the length of the video in the process of making the estimated score.

I came up with this:

raw_score - (raw_score / log10(video_length))

"raw_score" is a the estimated score mention above (1 to 10) and "video_length" is the length of the video in seconds. "log10" is the base 10 logarithm. However, this results in drastic penalties. For instance, it would reduce the score from 10 to 5 for a short video.

I am looking for some way to normalize this penalty so that the amount that a score gets reduced can be limited to a specific range, for instance at most 2 points.

What is the best way to tackle this problem?

  • $\begingroup$ If they watch 18 minutes of a 20-minute video, it means the score of 9. This is illogical. 8minutes watch score is 8 or what ? $\endgroup$ Jan 14 '21 at 1:13
  • $\begingroup$ @SubhashC.Davar well, the scores are actually calculated this way: the time watched divided by the length of the video, which would recult something between 0 and 10, and for the sake of simplicity, we multiply it by 10 and then round it. $\endgroup$ Jan 15 '21 at 7:44

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