As someone who used to work trafficking ads and spends a lot of time blocking them myself, I'm of the opinion that this is the approach to take.
ReadTheDocs and sites like BikeRadar are my favourite examples: If I go to a bike site, I expect the ads I see to be about bike stuff. If I'm on a technical site (like Stack Overflow or ReadTheDocs) I expect to see ads about related technical topics.
Unfortunately the advertising industry has gone down the route of hyper-invasive, user-data focused advertising, for what I consider to be very little benefit to either publishers or users. The only people who benefit are those who pedal the tech that enables this market.
With all the ML tools available to us now, I'm not sure why we couldn't build tools that accurately match ad content to site content. You don't need to invasively track users users this way either, because you only ever need to be concerned with the topic of the site/article and not who's seeing it.
We could build these things, but part of the "advertising host" optimisation metrics is "time to ad display" so the algorithms in use are frequently "deliberately dumb" for reasons of optimising the time involved in deciding what advertising should be displayed.
Web pages are far more static than users, your adserver/ML/analysis tool need only analyse the page once (not including updates for content changes) and then pass this information straight on to ad buyers.
Even if advertisers analysed the page content on seeing it, rather than the publisher, it would still be net faster than doing lookups/analysis per adserve (as you would when you target a user).
I don't think it would be hard at all to build something which analysed a page and made an adserving decision with a lower "time to ad display" than a similar process that used user data.
ReadTheDocs and sites like BikeRadar are my favourite examples: If I go to a bike site, I expect the ads I see to be about bike stuff. If I'm on a technical site (like Stack Overflow or ReadTheDocs) I expect to see ads about related technical topics.
Unfortunately the advertising industry has gone down the route of hyper-invasive, user-data focused advertising, for what I consider to be very little benefit to either publishers or users. The only people who benefit are those who pedal the tech that enables this market.
With all the ML tools available to us now, I'm not sure why we couldn't build tools that accurately match ad content to site content. You don't need to invasively track users users this way either, because you only ever need to be concerned with the topic of the site/article and not who's seeing it.