5 ms·

I have this problem with ML and recommendation systems in general. I hope this fad goes away soon and people go back to making simple, understandable, show-me-t

I have this problem with ML and recommendation systems in general. I hope this fad goes away soon and people go back to making simple, understandable, show-me-the-newest-stuff feeds.

isbjorn16 7y ago
Right - I just feel like it takes things entirely too far. Not every signal deserves a boost that takes a certain genre of suggestions up to 11. These recommendation systems need to be a bit less zealous; I like black pepper, too, but that doesn't mean I want a huge bowl of black peppercorns in cream sauce for dinner.
- SanchoPanda 7y ago
  They may be doing the math on extremes in categories where relative quality is good given their current catalogue.
- ryacko 7y ago
  People tend to look up movies not by theme, but by actor or director. Could Netflix be okay with categories that don’t fill up entire screens?
  - isbjorn16 7y ago
    That blows my mind; I never would have considered people would focus on the actors or the director. Until you said it I don't once thing I ever even considered the possibility! I'm kinda floored right now.
    - ryacko 7y ago
      Actors or directors are a decent indicator of quality. Recently I’ve found that old actors in mid-budget productions have the best overall quality.
SpaceManNabs 7y ago
Netflix didn't always have this problem. They seem to have changed their cold start model within the last 2 years.
Mirioron 7y ago
I think the issue lies with there being a single recommendation tab that gets filled in without enough variety.
foobarian 7y ago
One reason I appreciate broadcast TV is the curation aspect of their limited playlists. The streaming recommendation systems are very lonely - I know they are algorithmic and personalized, and that nobody else will see the same outcome. With broadcast, a human does that work and it is shared among viewers; e.g. some show always airs on Thursdays at 8. Then there is the seasonal cadence, i.e. summer shows, fall/spring shows. With algorithmic systems it sometimes feels like reading books written by a Markov chain toy.

I have this problem with ML and recommendation systems in general. I hope this fad goes away soon and people go back to making simple, understandable, show-me-the-newest-stuff feeds.

isbjorn16 7y ago

Right - I just feel like it takes things entirely too far. Not every signal deserves a boost that takes a certain genre of suggestions up to 11. These recommendation systems need to be a bit less zealous; I like black pepper, too, but that doesn't mean I want a huge bowl of black peppercorns in cream sauce for dinner.

SanchoPanda 7y ago
They may be doing the math on extremes in categories where relative quality is good given their current catalogue.
ryacko 7y ago
People tend to look up movies not by theme, but by actor or director. Could Netflix be okay with categories that don’t fill up entire screens?
- isbjorn16 7y ago
  That blows my mind; I never would have considered people would focus on the actors or the director. Until you said it I don't once thing I ever even considered the possibility! I'm kinda floored right now.
  - ryacko 7y ago
    Actors or directors are a decent indicator of quality. Recently I’ve found that old actors in mid-budget productions have the best overall quality.

SpaceManNabs 7y ago

Netflix didn't always have this problem. They seem to have changed their cold start model within the last 2 years.

Mirioron 7y ago

I think the issue lies with there being a single recommendation tab that gets filled in without enough variety.

foobarian 7y ago

One reason I appreciate broadcast TV is the curation aspect of their limited playlists. The streaming recommendation systems are very lonely - I know they are algorithmic and personalized, and that nobody else will see the same outcome. With broadcast, a human does that work and it is shared among viewers; e.g. some show always airs on Thursdays at 8. Then there is the seasonal cadence, i.e. summer shows, fall/spring shows. With algorithmic systems it sometimes feels like reading books written by a Markov chain toy.