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On Obsessive Recommendations

Thoughts about how to improve recommendation engines with regard to avoiding obsessions.

One of the challenges facing video platforms today is how to recommend content to users. As mentioned previously, my household recently switched to streaming in lieu of cable TV. On one of the services I watched a film in Spanish and the next time they were recommending me a bunch of Spanish-language films.

There have been a bunch of problems from YouTube creating rabbit holes of content where the recommendation engine over-recommends specific content.

Consider for a moment what it would be like to be transplanted to a world where everybody is obsessed with powdered wigs. You would constantly be hearing about wig powder, wigs, methods for upkeep. . . It’s tiresome just thinking about it. But instead we live in a world where only the wigfolk are obsessed with wigs. And they are obsessed. They go on video sites and watch nothing but wig videos all the time. The recommendation systems learn they want wigs, wigs, wigs. And the wig-lovers community is big enough that when someone new comes along who loves wigs, the system quickly recommends them a bunch of wig videos and they watch them all.

So your friend is reading about ancient times and happens to have a question about wigs. They go on the video site and find a video that answers the question. But now the friend is getting tons of video recommendations about wigs! Oh no!

Oh, yes. If we train the computers to be obsessed with obsession, they will try to find our obsessions. They will bombard us with every topic someone obsessed over and see if they can obsess us.

One wonders whether David Foster Wallace was right about Infinite Jest, but it will one day be an errant piece of video content that happens to find the nexus of everyone’s obsessions and the video recommendation systems will recommend it over and over so that it’s the only thing anyone can watch. But that’s not the point.

The point is that recommending content is fine, but it should not be obsessive. There has to be a better way.


The other side of the coin is when the obsession is what the user wants. If you have an account dedicated to video game news, you want it to be obsessed with that topic. You don’t want to see anything else when you use that account. That may be where some of the errant training arose in some of the recommendation systems.

The logical tweak to the recommendation systems would be to try to detect the type of account. Assume it’s a non-obsessive account, and only once the user has done enough to signal otherwise should the system switch over to obsessive mode. One would guess this would fix things for a lot of recommendation systems.

To go back to my Spanish film example, it’s likely that people who watch a single Spanish language film will want to watch a bunch of them. But maybe not, if they’re like me and only have a functional grasp on the language. So the second fix would be to bucket recommendations. Have a new grouping that says, “Spanish-language Films” and then let the user make the choice. After they show the preference for that bucket, the system can assume it was correct.

It reminds me of the old joke about a blind food test that got mixed up with a blind toothpaste test, and. . . you guessed it: four out of five dentists recommend Spot’s Dog Food (also, dogs hate toothpaste). Or, to put it the other way, if you put dog food up against bacon and steak, you’ll find out what dogs really want to eat. If the recommendation system only offers the user one type of content, there’s no guarantee that the recommendation system is worth a damn. It’s only when the user can choose the obsession over the myriad alternatives that you know (which, of course, is why faux capitalists always want to limit the competition—if your choice is dog food or more dog food, you’ll choose dog food).