Isn't sparse recommendation for videos kind of solved in netflix prize, where the winner uses SVD to extract signature characteristic and recommend videos base on that?
There are a lot of ways of formalizing the problem of recommendation. Perhaps the variant of the problem used by Netflix is "solved", but it's kind of an odd one. Basically, they built a system to answer questions of the following form: "Given that user X watched media Y, what rating would they give it?" They trained and tested on media that users have already rated. Some of the ratings are masked and thus need to be "predicted" for the test.
The issue is that the Netflix dataset has a baked-in assumption that a recommender system should show media that a user is likely to have ranked highly. It may be more important to show the user media they wouldn't have found (and thus ranked) at all. Or perhaps a user will be more engaged with something controversial rather than generically acceptable. Who knows?
Probably not. I say this because for many months, i would visit netflix and not want to watch anything. Eventually I cancelled my subscription after many years.
I think I'd rather have a random collection of titles than a recommended list for me.
I feel like if the Netflix Prize results had truly solved the problem, then they’d still be using them. It seems like the video recommendations aren’t as good as they were ten years ago during the prize competition, and they’re no longer based on what I might like but rather what Netflix wants me to watch.
The issue is that the Netflix dataset has a baked-in assumption that a recommender system should show media that a user is likely to have ranked highly. It may be more important to show the user media they wouldn't have found (and thus ranked) at all. Or perhaps a user will be more engaged with something controversial rather than generically acceptable. Who knows?