To me, the "best of the best" is its own thing. I do not watch romance movies in general, but I've had good experiences watching what people call the best of the best of the genre. Similarly for a lot of genres, media types, etc. Even just the "best of the best" of "cute animal video" can be fun every once in a while.

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 do agree a "I'm watching this but I regretted it" would be a useful button, if the companies respected it. But my default presumption is that the already know this, and don't care, and that baiting engagement / addiction is at the very least "views" and possibly a deliberate strategy.

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)

I didn't mind naive rating algorithms before statistics and recommendations got so gamified.

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.

The lack of nuance is killing all these sites for me, not just Netflix, which I gave up on managing. It's when sites carefully note what I look at, and start making drastic changes based on that.

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.

I extensively make use of incognito mode for YouTube when I want to watch something but not have it influence my recommendations. What's the closest analog for a paid service like Netflix? Creating an alternate profile for trash?

The title is a Russell conjugation, in case anyone has yet to be amused by these:

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

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

I like how this discussion has focused on Netflix where as the point is something different.

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.

I understand the frustration with current recommender systems, but what are the alternatives? Adding a bunch of arbitrary buttons or sliders isn't likely to actually improve the experience.

"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 wonder if most people like/don’t care about recommendations and don’t want to spend time giving companies more feedback.

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.

What's being referenced here is maybe the tip of the iceberg in terms of inadequacies of current recommendation systems.

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.

Social media websites often let you tag things with emojis or hashtags. This is more expressive, though people will disagree on their meaning.

that's very true for reviews of products, the classical five stars. Even with comments, they are seldom more than a data point, and stars themselves are given with very different meanings by different people.

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.

>There's no way to indicate, “I’m engaging with this, but I hate myself for doing it.”

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.

This is an unexpected yet welcome application of the fundamental attribution error: https://en.wikipedia.org/wiki/Fundamental_attribution_error

> “I’m engaging with this, but I hate myself for doing it.” I need another mouse button that is like, “I’m clicking on this, but I’m rage clicking on it and for my own mental health could you not drag more of this in front of me please?”

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

Why would people send the thumbs-up signal in Netflix if they didn't actually liked it? Just...do not click anything?

We've already forgotten the hype train that Netflix orchestrated when they held a paid contest for who can build the best recommendation engine. What was it - a million dollar prize?

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