a) YouTube doesn't know anything about the content itself, can only use metadata
b) The algorithm itself is biased towards creators that post often and keep users hook the longest, which is almost always vlogers (ask any animator what they think of YouTube)
c) Many recommendations systems today create many buckets and once you watch something from one bucket (you show your intent), the algorithm will focus on that bucket only. (You can see it working extremely poorly on Amazon that tries to sell you a fridge after you just bought a fridge).
It's very hard to build a great recommendation system (look at Spotify's Discover weekly), but because this is 101 of any machine learning course, it's the primary thing that companies refuse to outsource (I build company around it, failed badly).
If I watch "Some Video (part 1)", the recommendations reliably pick "Some Video (part 2)" next, with the other parts as the other related videos and similar content further down. If I watch a random video from a particular channel, the recommendations show more videos from that channel. If I watch a video of a particular game or a reaction to a given episode of a show, the recommendations show more videos of that game or more reactions to that same episode. If I listen to music by a particular artist, the recommendations show more music by that artist.
On the other hand, the front page consistently shows me 1) old videos I've already seen, 2) collections of highly viewed content that I have no interest in even if I've already hit the "not interested" X on it, and 3) popular videos by channels I already subscribe to (I don't want to know what's popular, I want to know what's new.).
> If I listen to music by a particular artist, the recommendations show more music by that artist.
I've had disappointing experiences of that, where the recommendations try to know what I'd like even better.
I've been building a search engine for lectures as a research project. For a small list of videos I find that browsing topic taxonomy is really nice compared to the recommenders that try to guess your intent.
There are commercial systems for automatically tagging the text (e.g. Watson) which hierarchies which don't go into niche areas - e.g. the Watson taxonomy tagger does 1,000 tags.
For more niche topics, I've explored Watson's entity recognition system, e.g. to recognize the names of diseases. The advantage is it picks up terms it hasn't seen- The problem is you can only identify entities that someone has trained a system to recognize.
The UI challenges are interesting as well. If spotified identified 100 genres that interested me, they could pick any arbitrary subset of playlists and I'd be pretty happy. If I used youtube to get home repair videos, and then they showed me videos about repairing parts of my house that aren't broken, it'd get pretty irritating.
BTW cool project.
d) The recommendations algorithm is one of the primary ways that YouTube users find videos to watch. No matter how bad its recommendations are, a lot of users will still act upon them, simply because the recommended videos are so visible. This becomes a self-fulfilling prophecy: videos that are frequently recommended are viewed many times because they are seen so often; the high view count on those videos makes them high-priority candidates for recommendation, and so on.
Youtube has one of the worst. Just today their 4th ranked vid for me was the 2 hr long 9/11/2001 broadcast. Wat? Then, they never boost new videos of a channel I subscribe to and have seen every video of theirs over the last 3 months. I literally have to check the channel most recent vid list daily to see if I missed something.
I think youtube's weak recommender system is more a result of them having a hammer(deep learning) and seeing every problem as a nail.
b) They chose the metric that made the most business sense. That's why if you spend 6 months working on your video and somebody else produces one video a day, you'll never show up anywhere close to top of the suggested videos.
c) Agree. Amazon has one of the better ones, but it's still terrible.
You hit the nail on its head with your assumption. This is in general Google's approach. But even with deep learning, the system is heavily biased, intentionally.
b) My guess is they're optimizing for viewership engagement with the added side-benefit of video-creator engagement.
Maybe the system is trying to convince you to buy a different, perhaps more expensive fridge
On a tangential note, ads. They sell those ads as "targeted" but when you play your "yoga" video... BAHM! a coca-cola add. So why build those "targeting" algorithms?
- Gather up all the channels that are followed by channels that I follow and/or have liked videos on.
- Recommend me videos from those channels.
I'm pretty sure in my case this would be much better results than what I currently get shown at any given time.
Imagine if your child asked an adult neighhbor about the movie "beaches" and they responded with the same answers YouTube does. Go ahead search beaches. Or Beach, or vine.
The rest are about beaches (most dangerous, weird things found on beaches, top 5 beaches in Brazil etc)
What is striking, and I've noticed this before on YouTube, is that the thumbnails all feature nearly nude women. You'd perhaps expect this to happen randomly for beach related videos, but I've noticed that if there even a fleeting bit of nudity in a film trailer or similar, it seems to end up in the thumbnail.
Does a human scan through and choose that moment, based on trying to maximise clicks? Or does an algorithm try random frames and then keep the ones that are click baitiest?
> Historical user behavior on YouTube is inherently
difficult to predict due to sparsity and a variety
of unobservable external factors.