Applications can be both economically valuable and have positive social impact. Now recommender systems are in fact useful to discover new things, not just to entice people to buy more. E.g. finding new music, related research, or other things.
Usually one of these:
- "I love the idea but can we talk about how the website doesn't look good when CSS is disabled?"
- "25MB of JavaScript? Really? You know, websites used to..."
- "This website is impossible to read because the contrast ratio is too low and I am personally offended."
- "Did anyone else get an ad from this news website? Why does advertising exist?"
- "An obscure open source project uses the same name as this unrelated business. Have you no shame?"
- "The website looks weird on mobile, by which I mean my rooted Android phone running Opera."
- "The text is too big."
- "The text is too small."
It's as though we moderns are all grazing in a 100-acre meadow that's been grazed for two centuries. The easy stuff is gone. Even the magic beans are rare. The question is: with your magnifying glass, which seeds do you look at in hopes that they're nutritious?
One of my college advisors muttered, and I quote, "Einstein wasted 30 years of his life" (looking for a unified theory). Well now. Sadly, this advisor put graphite on paper for a whole career with little to show for it (apart from employment).
Mr. Nobel's name is -not- still recognized because of his economically worthy applications. And it's lucky for Kelvin that he invented a temperature scale.
https://www.scottaaronson.com/blog/?p=3880
I like his update - a message to Hacker News commenters:
"On the Hacker News thread, some commenters are lamenting that such a brilliant mind as Ewin’s would spend its time figuring out how to entice consumers to buy even more products that they don’t need. I confess that that’s an angle that hadn’t even occurred to me: I simply thought that it was a beautiful question whether you actually need a quantum computer to sample the rows of a partially-specified low-rank matrix in polylogarithmic time, and if the application to recommendation systems helped to motivate that question, then so much the better. Now, though, I feel compelled to point out that, in addition to the potentially lucrative application to Amazon and Netflix, research on low-rank matrix sampling algorithms might someday find many other, more economically worthless applications as well."