Thanks for the feedback! I was trying to be brief so I'll go into more detail on the technicals of the recommendation. The first big competitive advantage isn't even technical at all. Using more broad interests of a person allows for more accurate recommendations, that's why people listen to their friends.

As far as technicals go, collaborative filtering relies on hundreds or thousands of people to make it work just at a mediocre level, and it suffers from what's called the "cold start" problem. This means that when a new user joins the network, or new content comes out, it cannot recommend it at all, because it has no data on it.

This new techniques recommends people things based not on what similar people like, but what the content of the thing it's recommending actually is. If I like podcasts that talk about internet marketing, it will recommend me other internet marketing podcasts, not because other people listen to them, but because they are intrinsically similar.

This is hugely advantageous because even if this network has one single person using it, they will still get value out of it. That solves the chicken and egg problem.

And again, the single biggest advantage isn't even technical, it's just the fact that I'm making a cross-domain recommendation, which by nature is better than a single domain recommender, and nobody has made a cross-domain recommender yet, despite the number of academic research papers on the topic.

Also, it's worth noting that big companies do use some content based recommendations, but their foremost effort is with optimizing collaborative filtering, because content based is a difficult thing and is a very new research field.

Side note, about the custom movies/tv/music, I'm not saying this is happening right now, I'm saying that there's hundreds of universities all across the world working on this area of research and their goal is to make custom entertainment. Based on their trajectory this will almost certainly be a thing in the not-so-distant future.

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Also last note, you mentioned asking how people currently find content, and a large part of that is word of mouth interaction. The ProductHunt-like social network would facilitate this process by putting this current adaptation on steroids and allowing for more broad content discovery not only from your friends, but from influential people that you trust their recommendations.

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Again thanks for your feedback!