A lot depends on what you mean by “algorithm of the same strength”. Youtube is a closed loop—they know how much of what things you watched, what you searched on, what you responded to and didn’t respond to. And they use that information to pay content producers in proportion to “success” of the content via their algorithms. And the additional feedback loop of knowing what videos you’re watching allows them to charge more for ads you’re shown.
It’s VERY good at optimizing for what it measures (people’s willingness to watch targeted ads around what content). I’d argue that’s more about data acquisition than algorithmic power. I’d further argue that it’s absolutely not what I want to be optimized in noncommercial interactive discussion spaces.
It MAY BE what I want in curated, directed, long- and short-form text publication. I could see Substack evolving to a similar model (where in addition to subscriptions to authors, you have it recommend per-read or ad-supported articles). I’d love it if an engine could aggregate dozens of magazines and publishers into that model, but I don’t think most of the current participants will agree to that level of central control.
(hmm does the lay-meaning of “algorithm” encompass the data, especially any ongoing recurring effects it would have. I think it must. A ML model is a product of its data.)
I think the trick with these systems is letting users talk back to the algorithm and help it out. Likes, or more meaningful signals of appreciation, help. Reddit go by without a recommender system because users were expected to essentially explicitly communicate all of their interests by subscribing to subreddits. Ranking is another way.
A lot depends on what you mean by “algorithm of the same strength”. Youtube is a closed loop—they know how much of what things you watched, what you searched on, what you responded to and didn’t respond to. And they use that information to pay content producers in proportion to “success” of the content via their algorithms. And the additional feedback loop of knowing what videos you’re watching allows them to charge more for ads you’re shown.
It’s VERY good at optimizing for what it measures (people’s willingness to watch targeted ads around what content). I’d argue that’s more about data acquisition than algorithmic power. I’d further argue that it’s absolutely not what I want to be optimized in noncommercial interactive discussion spaces.
It MAY BE what I want in curated, directed, long- and short-form text publication. I could see Substack evolving to a similar model (where in addition to subscriptions to authors, you have it recommend per-read or ad-supported articles). I’d love it if an engine could aggregate dozens of magazines and publishers into that model, but I don’t think most of the current participants will agree to that level of central control.
(hmm does the lay-meaning of “algorithm” encompass the data, especially any ongoing recurring effects it would have. I think it must. A ML model is a product of its data.)
I think the trick with these systems is letting users talk back to the algorithm and help it out. Likes, or more meaningful signals of appreciation, help. Reddit go by without a recommender system because users were expected to essentially explicitly communicate all of their interests by subscribing to subreddits. Ranking is another way.