But I have to keep a heavy hand on the YouTube watch history and remove many things that I may have enjoyed as an exception, but don't want to see endlessly offered up forever. Some of the strange attractors in the algorithm are very, very powerful, like the aforementioned "cute animal videos". Another problem I hit is situations like, I watched the video because, say, a parrot was doing a very good impression of Captain Picard (just making this up, sorry) which was given due to general sci fi interest, but the algorithm sees "a ha! another hapless human who likes cute animal videos! Après cette vidéo, le déluge!"

At least YouTube has that knob, and does generally seem to honor it. The algorithms that don't are very hard to keep from degenerating into the lowest common denominator, because the slightest hint that you like some extremely popular thing or have an interest in a very lucrative ad keyword almost immediately swamps my actual interests.

I think we need to start building intermediary feeds for all of our services (presumably via AI or something) that lets us customize how much we get of each type of content (e.g. no more than 5% about bad the economy is, no more than 5% about a new AI trick, 0% about 'drama of the day', 40% educational)

It's like if I go for a walk, metaphorically, and I see a giant pile of shit, and that's a genuinely interesting thing to see, because Jesus Christ that is huge. Then the algorithm notices I stopped to look at that, and the next time I go for a walk everything is filled with shit. That's what my Instagram feed is right now, metaphorically speaking. A never-ending field of shit.

YouTube and Amazon are some of the very worst. I regularly watch a video or song on YouTube (/Music) and explicitly say "I enjoyed that but don't want YouTube to focus on recommending me things like that so I'm not going to hit like".

Amazon, there are entire classes of links I won't click or products I won't search/buy because I don't want them filling my recommendations forever.

And no, having to manually dig through settings and find a list of my entire history to manually delete entries isn't a good workaround. Too much effort, easier to just not give the platforms the data in the first place.

https://en.wikipedia.org/wiki/Emotive_conjugation

https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

"People who liked this also liked this" is already a pretty good way of discovering things. The problem is when the catalogue is crap. I get plenty of good recommendations on Prime Video and Criterion Channel, which both have a substantial number of great films available to watch.

I suspect there's a range of indices related to content interaction that companies use poorly or even maybe nefariously — for example, are they motivated to present what you want, or what will keep you engaged with the site? Are their assumptions about why, say, you're spending a lot of time on a video correct? This post is focused sort of on options to communicate with the recommendation system, but there's a lot that could be said in terms of mismatched system-user goals in the system, and poor assumptions being made by the system in general.

I wondered too as I was reading it whether it's worth making the distinction between "different features of user experience with the content" and "metaresponse". That is, I can feel positively and negatively about the same video, or like it for one reason but not another; at the same time I can want to provide a response explaining a response. There's a difference between providing information about how I feel about some content, and information about how I want that information to be used.

The data people know this, the executives do not. Or rather, they choose to ignore it, because they prefer metrics be as simple as possible.

They even try to reduce numeric values to boolean ones. When you get asked on a survey to rate from "Very Unsatisfied" to "Very Satisfied", they often reduce that to a percentage count of people who answered the top 1 or 2 boxes.

I worked at a medical company where the CEO was very enthused with the idea of having large thumbs up and down buttons at the front desk, right in front of the staff. What worked for convincing him this was a bad idea was a story about staff at a retail store panicking because a child started banging on the thumbs down button. Customers got an email with thumbs up and down instead.

I work in streaming recommendations and basically no one voluntarily gives extra feedback, and most initiatives that aren't machine learning based on true behavior fail on A/B tests.

There are 2 useful rules to consider when building stuff: * Your user might not necessarily be like you - this applies even more when we tech people consider ourselves to be like our customers - we're often very different. * While in individual experiences your click of a thumbs up button constitutes a yes, and ...; if we're talking 128k likes, the aggregate dulls some of the edges and has interpretative value.

that would be a thumbs down if it exists. The system already knows you're engaging with it, they checked that you stopped scrolling and did all sorts of stuff around the thing you are engaging with.

So when reviewing reviews, care is needed. Simply picking the product that maximizes stars and number of reviews is a bad metric for the quality of a product, that is, for what your perceived quality of that product will be.

That's why I look at two things: 1) distribution of reviews; if frequency of stars decreases monotonically with star number, it is probably a good product, and I look at 3/4 star reviews for pro and cons; 2) if the distribution has a spike in the 1 or 2 stars, I further investigate to see what the problems are, regardless of number of reviews or how many five star the product has.

So far has worked great on Amazon.

The same way bakeries and cafes have zero incentives to not be your local sugar addiction dealer.

Why would they? They do not care, why you engage, they want you to engage as much as possible.

It was shit back then, and it is still shit today. It just got worse over time.

I use a custom filter in uBlock Origin to hide the recommended sidebar in YouTube (all except the video of what’s next). Even so, there will occasionally be a video in search results with a topic that might be interesting from a channel I don’t normally watch.

I’ve learned to right click and open the link in a private browsing window so that my search results (and that one remaining sidebar thumbnail) do not become poisoned by that one channel/topic.

It’s insane that clicking on one video makes YouTube think that I’m now obsessed with that channel. And, it’s going to fixate on that for the next few weeks.

This happened to me with youtube and feral hog trapping. A two-minute video of that showed up in my feed, and yeah, it was interesting to see how and why it was done. But my feed was full of that for a long time, and even now, years later, one will pop up just desperately trying to get me to re-engage.

It was interesting as a one off, not as a way of life.