I visited a friend who is watching Breaking Bad. Now clips appear on my YouTube?
We watched an episode and now clips are showing as recommended on YouTube. How does it work?
(Edit 16:48Z) More info: - using iPhone - YouTube on Safari, no app - got connected to her wifi
130 comments
[ 3.3 ms ] story [ 188 ms ] threadI'm willingly to believe it was coincidence, but I am suspicious.
- I walked by an e-bike store, stopped to look at the model displayed on the window, a few days later I got ads for e-bikes on Instagram (I can't remember now if it's for the particular store's brand).
- I opened Instagram at a carwash, a few days later, ads for car-detailing services - here I have a strong feeling it used my location data.
https://blog.citizennet.com/how-to-measure-store-visits-on-f...
> By using location services on cell phones and 3rd party satellite imagery and mapping data, Facebook is able to tell you if someone visited your store within 28 days of clicking on your ad, all while filtering out employees or people who move past your stores without going in.
> Facebook can't measure all store visits. This is because some people don't have location services turned on in their phone, or are not recognizable by 3rd parties.
https://www.placeiq.com/audiences/
> Reach audiences who visit your location on a regular basis
> Reach audiences who regularly visit your competitor’s locations
> Message audiences who regularly commute past your location
Data is usually refreshed monthly. He may also see ads for things you search for.
Unfortunately, there's no way to confirm that since the only phone out there with a hardware microphone switch is the Pinephone.
It’s the same method Apple uses to check for “unauthorized internal API use” people get busted for.
We can also replace the AVAudioSession class with our own proxy to examine calls.
I know they claim they don't, but do you have reliable information that confirms this? Like experience on an internal team at Google/Apple?
Better to make listening on the mic illegal, punishable by prison time for the execs.
I am not a fan of problem-solving via legislation, but I wouldn't lose a lot of sleep if Big Tech was no longer allowed to burn billions of CPU cycles on making opaque, inexplicable guesses as to what ads are most likely to make them money if shoved in front of my eyeballs.
Mass ads means more money goes into ads and ads are irrelevant to many people. Only big co’s can afford mass ads. Targeted ads means ad money is vastly more efficient, and your information environment is less cluttered. One-man shops can afford targeted ads.
I like seeing relevant ads. I like supporting small businesses targeting me. Privacy isn’t a concern either because the three-letter agencies can find out whatever they want about me if they want anyway.
FWIW, I’m not in advertising.
Found they've completely replaced the order taker with a robot, with order signage saying "help train our robot".
No opt out, and you can't get to an employee unless it recognizes the word employee. Recording in progress.
The ADA non-compliant parts aside for people with voice related disabilities, its BS what companies are forcing people to do just to buy a burger. I'm not going back after that.
Maybe you left that out because it already happened.
One simple way you could build such a system is to come up with a list of things about movies that might affect whether or not someone would like them, where for each thing on the list we can assign each moved a number from -1 to 1 that says how much of that thing the movie has. Call this list the movie's vector.
Some examples of things we might pick are how much comedy is in the movie, how much romance is in the movie, presence of A-list stars, how musical it is, and thing like that. We might also have items for specific stars or directors.
Then we could go through our movie collection and have someone figure out each movies scores for all those things in our list.
Then we could figure out for our users a list that lists for each of those things how important it is to that user, from -1 (I hate movies that have this!) to 1 (I love movies that have this!). Let's call this the user preferences vector. If we have a list for a given user of movies they have already watched and how they rated them on say a 0 to 5 scale then it is some straightforward math to figure out the user preferences vector for that user that does the best job classifying the movies they have already seen in a way that agrees well with that user's ratings.
That user preferences vector can than be used to recommend new movies and should work pretty well if (1) we picked a good list of things to score movies on, and (2) when we manually assigned the scores we got it right.
To predict how well a user would like a given move we just take their user preference vector and compute the dot product of it with the movie's vector. The more positive that result the more we think the user would like the movie.
With this system, it would be easy to tell someone why you recommended a movie. We could look at their preference vector and compare it to the movie and tell them things they really like that the movie has and things they really hate that the movie does not have.
But the system described above has a drawback. It is hard to figure out what factors to include in the movie classification. Should comedy for example be one item, or should it be broken down into several such as physical comedy, insult comedy, bodily function comedy, and so on?
Also, if you have a large collection of movies it is a lot of work to go through them all and score them on each factor. And if you later find out you need to add or remove factors you have to do it again.
It turns out that there is a way to sidestep both the "what should my factors include?" and "how do we get the factors scored?" problems.
What you do is just decide on how many factors you will have. So let's say we decide we are going to have 50 factors. We don't have to decide what they mean. We'll just call then F1, F2, F3, ... for now.
Initially we just assign each factor a random value from -1 to 1.
We also do the same thing for the initial user preference vectors. Just assign each factor in the preference vector a random value.
Then we can do a loop, consisting of these two steps:
1. Using the known 0 to 5 star ratings from users of films they have seen, adjust their preference vectors so that ordering movies by the dot products of the movie vectors with the preferences vector matches the ordering by the user's star ratings.
2. Same thing, except instead of adjusting the preference vector to better work with all the movies a user has seen, adjust the movie vector of each movie to better work with the preference vectors or all the users have have rated that movie.
Keep looping until things aren't changing much. You then end up with a set of movie vectors for you movie catalog and preference vectors for your users that do a good job of ordering movies that user has seen that matches well with how the user rated those movies, and that likely does a good job predicting how well they will like new movies.
This ...
Twitter is now showing me kindle ads for those books.
Twitter has a menu offering to explain why I see it. The explanation says: Amazon wanted to advertise this to people in your region.
I say they lie… We need Blackbox monitoring against those behaviors with some legal teeth.
- Random coincidence, which you notice in the one case where it happens but not in the 99 cases where it does not.
- You did search for it or something related. Or, you got one clip due to one of the other reasons, clicked it, now you're getting more.
- Correlation via IP
- Correlation via location
Since we're talking about YouTube recommendations, not ads, I kinda doubt the last two though. That would provide very little benefit and be a huge privacy risk. Location is certainly considered to some extent, but I would expect this to be on a country/region level, not city and certainly not fine enough for your friend to meaningfully influence it.
I also completely believe that YouTube correlates via IP for at least not-logged-in views (or at least tries to associate to accounts even if they've never logged in); I get bleedover to my iPad from my completely disassociated PC but not my Mac that’s logged into a different account.
(Also maybe I put too much effort into tailoring my own YouTube recommendations, but 99% of the time when they start going awry, I have a pretty good idea what triggered it. Random coincidences don't happen...)
Interestingly, I seem to recollect this happening only in the past few months or so, maybe a feature they turned on recently?
I'm frankly surprised to find some people in this thread don't already know that ip correlation is going on as a matter of course.
In addition, there could have been 100 instances in the past but they didn't stick.
To the contrary, I highly doubt that they don't use it, privacy risks be damned. Using that data is their primary business model after all.
Easy enough to test, just start talking with another person for about 2-5 minutes straight repeating the words purple dog collar over and over in earshot of the device you suspect.
You'll be amazed when within 30 minutes to an hour all the ads on all devices will suddenly show purple dog collars or pet related items.
- The system knows you two are friends (there are multiple ways for that), and you get recommendations based on what interests your friends
If you watched the episodes on a device connected to your Google account in any way I think the answer would be obvious.
Another reason you might be seeing ads for it is that you might've looked up an actor or character on your phone which Google linked back to the show.
It's also possible that Google noticed your account was suddenly shared by someone who showed interest in the show and "infected" your account.
Another possibility is that Google picked up on the show if you used a Chromecast on the same network to watch an episode; media controls are broadcast throughout the network.
There are more far fetched reasons. For example, if your location history is on and the neighbourhood where your friend lives is seeing a sudden spike in interest in Breaking Bad (watch parties? Idk), you may have been flagged as interested purely by geographical proximity.
It can also be a combination of Baader-Meinhof and your behaviour around the ad (looking at it longer, possibly unintentionally out of surprise) that reinforced the topic in your ad preferences.
Tl;dr, Google can learn a lot about you through indirect means. It's kind of their business model. Compare to the famous case where a supermarket knew someone in a US household was pregnant before the other family members did based on purchasing behaviour. It's scary how much you can tell about a person from their shopping patterns alone and Google has their hands in so much more.
None of this should probably be happening if you've disabled personalised ads (https://adssettings.google.com/), at least not on Google's platform.
It's probably just trending topic and you fit a similar profile to people that would watch the show/Breaking Bad. My viewing habits are pretty stable and I noticed I get recommendations for The Wire even after resetting all my cookies as well as not logging in to my Google account
or more likely, you googled something breaking bad related while watching or talking about it.
or even more likely, it's a coincidence. my youtube recommendations also had a breaking bad clip in them yesterday, despite me not watching it, talking about it, or thinking about it. better call saul season ended recently, and people are searching for breaking bad clips. it doesn't have to be more complicated than that.
I think it's random coincidence BB is getting traction now.
(Before BB was The Sopranos clips)
Unless you've already completely de-googled your life, this is the most straightforward way to link you to the activity. All it would take is for you to have pinged Google's servers with that phone just once, ever, and they've got it fingerprinted. So when it shows up on the same network accessing YouTube, they placed you.
One use of Google Maps, Google Home, logging into Gmail or YouTube is all it takes to compromise a phone.
tldr: Reason 1: Wifi cache. Reason 2: Microphone tracking.
I shared a car ride with a friend for 20 min, and my colleague, being knowledgeable in trivia, mentioned an obscure historical figure that I couldn't even spell the last name of. I came back home, and on my Google feed there he was, the obscure historical figure with his last name fully spelled out in a biography.
I never connected to my colleague's network in any form (we were in his car); I didn't search for this historical figure (I don't even know how to spell the name correctly). And most definitely not a coincidence as I had never heard of this person before or after the brief mentioning by my colleague. I don't think my colleague searched for this person on his phone recently either as it was some trivia knowledge he randomly recalled.
This happened several times with this particular colleague as he likes to mention random trivia. I have yet to find a plausible mechanism for this phenomenon (unless it is an open mic).
e.g: it's using location-tracking to link you two, not an open mic and realtime transcription.
PS: my understanding is that a lot of social media sites use a strategy where they somewhat randomly push single posts by reliable creators, to make sure that those stay hooked as well. So that could be an explanation why you'd suddenly see something a bit more niche.
On the other hand, the obscurity of the feed subject makes me think YT/Google wouldn't be pushing those on a broader audience, but maybe the algorithm is just that good at nerdsniping us.
Later that day I see an advert for said obscure topic served on a web page.
Is it possible that there is someone transcribing podcasts or at least scraping databases of their RSS feeds and somehow my music player app is broadcasting that I've listened to a particular file(after receiving an internet connection)? The alternative is that the machines really are listening.
1.https://en.m.wikipedia.org/wiki/Frequency_illusion
1: https://en.wikipedia.org/wiki/Frequency_illusion
Visit a country that speaks German? Advertise in German.
Seems consistent to me, not a contradiction.