Define model with explicit positive feedback and explicit negative feedback

Hey, I am working on a problem where I have 4 different feedback signals. I am trying to recommend new videos in a playlist to a user.

My dataset is such that I have pre-defined playlists categories with each category having predefined possible videos. Now the problem is to recommend new video to a user. A user can watch a video, like a video after watching it, skip the video in the middle and abandon/leave the playlist and move on to a new playlist.

My goal is to recommend those videos that are liked by user or watched by user and avoid those videos that are skipped by user or after which user left the playlist. So I have four feedback signals available at hand

1. Implicit positive feedback given by watched videos
2. Explicit positive feedback given at the end of video
3. Explicit negative feedback given by skipping of video
4. Explicit negative feedback given by abandonment of playlist

Now how I should go about structuring the model?

Following #139, it's clear that I can define two retrieval task, First one for Implicit positive feedback and the second one for explicitly positive feedback.

But how should I use explicit negative feedback? I looked into #232 but that didn't provide an answer.
I was thinking of doing

Start with the retrieval-task discussion in #139 and the negative-feedback discussion in #232; the issue provides no file, test, or code entry point. Compare how watched, liked, skipped, and abandoned events should be represented, and define a model approach that distinguishes all four signals. Done means the design is settled and actionable for implementation.