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Comment by reggieband | Hacker News Reader

I don't have time to read the entire paper but I would like to share an anecdote. I worked at a company with a well staffed/funded machine learning team. They were in charge of recommendation systems - think along the lines of youtube up next videos. My team wanted better recommendations (really, less editorial intensive) so the ML team spent weeks crafting 12 or more variants of their recommendation system for our content. We then ran that system for months in an A/B testing system that judged user behaviour by several KPI. The result was all variants performed equally well within statistically insignificant bounds. The best performing variant happened to be random.

Talking to other groups that had gone through the exact same process our results were pretty typical. These guys were all very intelligent and the code and systems they had implemented were pretty impressive. I'm guessing the system they built would have cost a few million dollars if built from scratch. We did use this "AI/ML" in our marketing so maybe it was payed for by increased sales through use of buzz words. But my experience was that in most limited use cases the technology was ineffective.

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This reminds me of a job interview I was on. I was asked about how I would use AI/machine learning for their problem space. Since they seemed to be smart and level-headed, I answered honestly, "Pick something unimportant, use a machine learning algorithm just to get familiar with the tools, ignore the result unless it happens to work, then put machine learning in your marketing materials. But keep track of it, and if it is useful to you in 5 years, be sure to use it for real."

They said, "That's about what we concluded except that we didn't get around to actually doing that pilot project yet."

I got the job. :-)

That sounds odd, like they don't really need machine learning (unless it is to snare investors?).

Snaring customers too. I swear, people are obsessed with "machine learning" even when the domain really isn't suited for it.

I sometimes wonder if management and engineers don’t have more in common than acknowledged. Publications such as Harvard Business Review have huge coverage of things like managing AI, and being able to say you managed an “AI project” might mean something.

I wonder if people are becoming more savvy and anti-ML after all the issues people have with Facebook and Google collecting a lot more data than people are comfortable with.

I suspect what people dislike is big data. Anecdotally, I'd love to hear about a success where some lone genius codes a better tool using publicly available datasets. I don't like the idea of BigCorp using their larger servers to dominate the space.

I think generally that is how many products and features work.

Speaking of jobs and interviews. I am yet to find a job board which does not show JavaScript jobs when searching for Java jobs. Some of them claim to use AI. :)

In fairness I have this issue with a number of human intelligence based (allegedly) recruiters.

Of course, that's where machines gets it from as well. Your training set is usually which candidates were shortlisted by recruiters. At least that is what I have seen people do who are built one of these AI based recruitment systems. When I asked them are they not worried about such bad quality data, their answer is - humans know the best. Ah well, then why are you building this again?

My wife once watched me as I did a job search, carefully setting all the available parameters to match my requirements. She said "don't forget, engineering jobs are posted by admin staff", typed "software engineer location" into Google, and found way more than I ever did.

Let's not forget the job application systems that make you chronologically list every previous employer, their location, your title, and dates of employment, your education history including dates and degrees received, skillsets/technologies you have experience with, etc. All information that is on your CV and/or LinkedIn profile and they want you to manually re-type it into their late 1990's era job application system rather than using some basic NLP to extract it. Personally, the moment I start applying and the system asks me for more than my email/mobile and a CV upload, I bail on the application.

I guess that is intentional to throttel down on people applying. It works for me: Except, when I desperately need a job, I don't apply. I primarily only know "this" style of applying - It's all big corp companies, though, I must admit.

The exact same applies to blockchains.

Blockchain is mostly a marketing tool, not something you would want to use in production for anything.

Since we are sharing anecdotes, I can report it's been 20 years of buying stuff on the internet and the combined billions of ad tracking research dollars spent by Amazon and Google have not yet come up with a better algorithm than to bombard me with ads for the exact same thing I just bought.

This can happen when a retargeting campaign doesn't have a 'burn pixel' or conversion event trigger. It's a common oversight, which can cause a re-targeting program to kick-off unnecessarily (or cause ads that have followed you around to become obvious)

The AI innovation must be that they can figure out which marketers are likely to forget to have a burn pixel because those marketers drive more revenue.

You'd think with all the "AI" out there, they could match a sales DB entry with the CRM DB entry, but, in fact, they basically can't.

And yet I haven't seen one ad that has a 'not interested' feature similar to YouTube.

I mean, if I could stop seeing washing machines or whatever, I'd probably click it.

do you clear your cookies and html5 storage? That should wipe any personalization that's happening, but the ads will become very generic then.

You can also block ads (Ublock/Umatrix)

Oh yeah I can do that, I'm just wondering why they're not too interested in direct user feedback from my end. Surely it would be useful to adjust their algos.

Right? Recommenders are almost counter productive for me in most cases. I want a recommender to remain broad, not give me increasingly niche recommendations a la youtube. About the only halfway decent recommender system I've interacted with is the various forms of curated lists on Spotify. They seem to actually take a decent stab at it with related but sufficiently different and interesting content.

For all Facebook knows about me they've always been exceptionally bad at advertising to me, which is remarkable considering what they've got. Google is only very marginally better. Actually, now that I think about it, Amazon's 'customer's also bought' is also pretty bad at the recommendation itself since it not uncommonly recommends incompatible things! ...but it does often succeed at getting me to think more about what else I might need and sometimes leads me to buying other things. At least it's not always recommending the same thing, but rather related things, which is probably a much better way to advertise.

Likewise Pandora does a decent job of picking songs for me based on what I previously liked. It's not perfect but far better than random.

it's easier to recomment things you probably like than things they want you to like/may like

> For all Facebook knows about me they've always been exceptionally bad at advertising to me

Because it's not Facebook really, it's the advertisers who choose targeting criteria. You as an advertiser have a myriad of options. For example if you've built a competitor to X, you can target users who've visited X recently, aged N-M, residing in countries A, B and C, and so on. There are options with broader interests too. Poorly targeted ad means poorly selected criteria by the advertiser (or sometimes just advertiser experimenting) and consequently money wasted. Facebook doesn't care though.

Then there is retargeting/remarketing (target bounced traffic) already mentioned here is probably the stupidest looking invention that actually works.

But they are also really really bad at curating content I like to see. Like even worse than the advertising actually. My feed is just garbage and has been ever since they switched over from being chronological. But I sometimes get on a wild hair about some particular person, and I'll look at their page directly and see actually interesting content on their page that was never shown to me. Facebook's only real guess is then to just say: oh! You must like this person, let me show them, all the time.". The reality is, I'm interested in stuff like when a biased person I don't like shares a more neutral or inclusive opinion, or someone does something interesting. Facebook is just unuseably bad at selecting for that. Their algorithm just pushes really shallow crap and buries anything with substance or depth. I keep it around just to stay in touch with hard to contact people.

What I've always assumed, but now this thread has me doubting myself, but what I've assumed is that these systems, even though they appear to suck at specifically targeting me, must somehow be pretty good on the average, still netting big profits overall even though they don't seem to live up to the promise of getting me to buy stuff. But if everyone has this impression, maybe it doesn't work? I mean, I assume companies like Amazon and Facebook would be pretty good at least at optimizing the total rewards from using recommendation systems, but maybe you can't tell from your own anecdotal use. I have no idea. I'd love to see an analysis that includes aggregate numbers.

I think you've got the nail on the head. It's sort of the tragedy of the commons, which in terms of recommenders is a really easy thing to accidentally optimize for. For example, pop music is popular. In the early days, recommenders would just recommend pop music because on average that was a decent recommendation. We've come a long ways since then. Well, everyone but Facebook has.

That's because:

* They don't have suppression set up

* They're using a conversion tracking platform that is slow

* They're testing the returns conversion hypothesis: you have expressed concrete intent, you have bought the product. If it has 5% return rate, you probably still want it, and there's a 5% chance they need to be in the mix.

I just spent 15m on Amazon trying to prod the recommendation algorithm into finding something I actually wanted to buy so I could get above the "free delivery threshold".

Think about that. I wanted to spend money. I wasn't too fussy what it was. Amazon has a decade of my purchasing and browsing history.

And they still failed.

Ha. Last time I wanted to buy something on Amazon, their search page kept freezing every time I load it: 100% CPU load. Because I really wanted to buy that thing, I spent 30 mins debugging their silly scripts and found one for-loop that tries to find a non-existent element. Unfortunately, I couldn't figure out a way to enable my fix in a minified script, as reloading the page kept loading the original script.

Whit uMatrix, you could have created a rule to block that script.

Put a breakpoint on the offending line, manually add the element when it breaks, hit resume and hope for the best?

Turn off Javascript - Amazon still works OK.

don't gift card count anymore?

Amazon still suffers from those "This guy really loves washing machines!" type recommendations

I hope 'm' here is minutes, not millions )

Amazon infuriates me. I regularly buy gadget-y bits like electronic components and peripherals. Probably at least once a month. Never see adverts for similar.

That ONE TIME I buy a unicorn dress for my 2 year old daughter? That's a lifetime of unicorn related merchandise adverts and recommendations for you!

Actually that's really interesting because it exposes a bias in their recommendation system. They must be heavily biased towards things with mass appeal instead of specifically targeting user preferences, which is funny because it goes against the grain of the whole "targeted advertising" promise of ML. You'd think if anyone could get that right, it would be Amazon, yet..

This is basically what comes from a mindset increasingly common among ML practitioners of abdicating thinking and assuming "the machine will find what features are important". They throw a junkyard full of features at the algorithm (or even worse automated feature generation). These days at least once a fortnight I get the opportunity to show folks how if only they thought for 10-15 mins or simply charted their data in a few different cuts before modeling, how much better they could have done :-)

I suspect Amazon has learned that some feature labels are easy to recognize and correlate (dress color, size, style, etc) and others are hard and lead to useless results (an electronic device's: computer, format, port type, protocol, etc).

So they gave up trying to match CPU with GPU and went back to connecting beer to diapers.

It's not impossible for that to be a clever decision by Amazon (although I'm not saying it's likely, I have no idea about the numbers).

The ultimate goal of the advertising is return on investment, not making you feel interested in the adverts. If, to exaggerate the possibility, 100% of "people who look at tech" are 0% influenced by adverts, but 10% of "people who bought a unicorn thing" will go on to buy another if they're constantly reminded that whoever they bought it for likes unicorns, all of a sudden it would make sense despite being counterintuitive to viewers.

A more commonly discussed example of a similar thing is that it's easy to think "I just bought a (dishwasher, keyboard, etc), I obviously already have one so why am I seeing adverts for them?" Sure, it might be that the company responsible has an incomplete profile and doesn't know you bought one already. But it's also possible that the % of people who just bought the item and then decide they don't like it, return it and buy a different type is high enough to be worth advertising to them.

Hey, personal experience and to be fair for them, Google does occasionally give me ads about a Haas CNC machine. I really want one. But I don't have the disposable $100k for one and I don't have the space... nor do I have 3 phase power. But I do want one and haven't bought one. So, good on them, right?

The marketing budgets are there to be spent :)

Yes - just in case you want two or stopped the purchase along the way.

Sometimes it also recommends other brands of the thing I just bought. Like a cell phone: I didn't buy Samsung, but now I see their ads. I guess that is a very very small improvement- Maybe I'm the type of consumer who gets a new phone every week?

No, but you might be unhappy with your phone, return it and buy a new one.

I have been looking for a shelf that’s as close to 50” wide and 10” deep as possible. No search on any site that allows you to search for such a thing as far as I can tell.

You can do this at wayfair.com. It's kind of hidden though, when you get to the shelves, click "Sort & Filter" and then scroll down until you see the dimension sliders.

I think amazon works pretty well.

They show me similar things to the thing I just put in my cart. Sometimes better choices that I made.

They also show related things that other people have bought, that many times I end up purchasing.

Dirty secret: "The customers who bought also bought" algorithm doesn't require ML/AI.

You can accomplish that with relational algebra on a precomputed data warehouse job and only for products with strong correlation. The intelligence of the customers is enough agency to instil a semblance of intelligence in the data.

Yes, this is the straightforward ‘collaborative filtering’ algorithm. I suppose the line between ‘algorithm’ and AI/ML is not well defined though. At what point does a technique become ‘AI’? I don’t know a good answer.

As an utterly cynical layperson, algorithm means directly querying data. AI means feeding systems with training data and sprinkling them with magic obfuscation dust.

I wonder if the dirty secret for lots of stuff is: "They aren't using AI anywhere"

Well, for problems that are simply looking for a ranked relationship if you have human input it could be used to train a ML that attempts to look for similar correlations...or you could just use your human mechanical turks that are already informing you. ML problems that are good are the ones not trying to approximate reality like CWBAB.

I'm doubtful we'll see an ai that makes a serious jump without directly interacting with the world we live in. Under that measure cars might be the closest since you learn to interact with the bounds of where it can and can't go is similar to a toddler learning to crawl.

Not to mention these systems are also completely useless at recognizing one product someone bought already includes the product they're recommending. When you buy Dark Souls 2 with all DLCs you can be sure Steam will suggest Dark Souls 2 without any DLCs to you for at least a full year.

You are luckier than me: I've spent 10 years bombarded with ads for things I never bought, never wanted to buy and that are directly insulting at this point. (Buying something would maybe give me a two day break though.)

I have had a similar experience, but I really do think it points more to the team than it does to the efficacy of machine learning. I was on a team of extremely intelligent people, but they were very academic, with minimal practical coding skills, refusals to use version control etc. By academic, I mean graduates from the number one university in their respective fields, top publications, etc. They produced very little actual value. Great theoretical ideas, in depth understanding of different methods of optimization, etc.

The team I was on before that one was a bunch of scrappy engineers from Poland, India and the USA with no graduate degrees, but 20 years coding and distributed systems experience each. The difference in problem solving ability, the speed at which they moved, broke down problems, tried out different methods, was staggering.

I think ML is suffering from a prestige problem, and many companies are suffering for it. The wrong people are being hired and promoted, with business leaders calling the shots on who runs machine learning projects without fully understanding who can actually deliver.

The San Jose Mercury News had a weather-forecasting contest. It was won one year by a 12-year-old, who's algorithm was "The weather tomorrow will be the same as the weather today". A kind of AI I guess.

Using a markov chain with your stochastic matrix set to I...

Using “a I”

To be fair San Jose weather is heavily biased towards sameness.

Yes, but why did all the 'smart' people using models and training networks get a worse result? A condemnation of modelling, to be sure.

I have a friend who lived in San Jose who just wrote the forecast on his whiteboard and left it there, because it never changed. It was funny, because he came from Minnesota where the weather is never the same two days in a row.

Ha! I came here to say something similar. Here in San Jose, the weather tomorrow will be the same as today, most of the time. My joke when I lived in Minnesota was: "If you don't like the weather, just wait 10 minutes. It will be different."

Gotta say though, I don't miss my slow blower even just a wee little bit.

Brilliant. I think YouTube has arrived at the same algorithm - it picks the videos I watched yesterday to recommend today.

Completely ignoring the depth of your subscriptions as well. Amazon music was much better for me. Even Google music just repeats.

Seriously. Why would I want to watch a video that I've already watched (unless it's music maybe)?

They have a lot of videos. You're statistically unlikely to care about any randomly selected one. By watching a video, you establish that it's interesting enough for you to watch. It's much more likely that you'll want to rewatch it than that you'll want to view a random video.

I'm only half-joking here. To me, YT algorithm seems to be a mix of "show random videos you've already watched" + "show random videos from channels and users you watched" + "show the most popular videos in last few hours/days/weeks". It's pretty much worthless, but what are we expecting? Like all things ad economy, the primary metric here isn't whether you like the recommended content, or whether that content challenges you to grow - it's maximizing the amount of videos you watch, because videos are the vehicles for delivering ads to you.

I prefer a user driven random walk. Like a multi-arm bandit over a hierarchical graph instead of a stuck-in-local-minimun + noise recommender. But no one does it.

Years ago, there is an app stumble upon. I always find it engaging. Hard to get bored.

Im guessing things like 5 year olds watching the same video 300 times randomly on their parent's account has irreversibly tainted the Googletron into thinking people like to watch the same video over and over again.

Hundreds of times I have told youtube im not interested in a recommended video that i have already watched, seems to be completely ignored.

Music is probably the main driver, but I've definitely clicked some recommended videos from a channel I'm subscribed to with infrequent long uploads.

Why wouldn't you? Do you never re-watch a film or re-read a book or re-order the same food at a restaurant, or re-drive the same route to work?

I often watch the same video that I've already watched, many times music, many times comedy, many times something I want to link to another person but end up watching some/all again as I find it, often if I remember it being interesting (e.g. a VSauce video or a Dan Gilbert TED talk), sometimes if it was a guide or howto that I want to follow - e.g. a cooking instruction.

Well to be fair that's how all employers also hire.

If you did a good job at the last company you'll probably do a good job here.

If you did a good job yesterday, you'll probably do a good job today.

For the most part they are usually correct.

I hope you are joking since the industry collectively knows how much employers/interviewers value algorithms-based coding interview, which doesn't correlate strongly with performance. Even if you are talking about senior positions where they don't matter, then you should know that people hire someone they know+like who did decently well, rather than the truly best on the market.

well, that's the thing though - coding interview is pretty much a yes/no thing. What rank you get is typically based on "what rank do you have now?" and "how much do we respect your current employer?"

That's SF/Big Tech. The rest of the world basically works like the stated algorithm.

The coding interview comes usually after they vet your resume, any profiles, get you talk to an HR, and potentially check your references.

Even the coding interviews are just a signal against overall performance given a short amount of time. It's only a sample of data, but if you did your interview right you should be able to protect some against bad people getting very lucky. Just like driving a car; bad drivers tend to stay pretty bad, and good drivers tend to stay safe. Even though there's a lot of ways to define what is a good driver, there's clearer ways to define what is a bad driver, and if someone was a bad driver yesterday they are still probably a bad driver.

I remember reading Steve Jobs used a "different" technique to figure out if someone was good.

He would go around to people and say "I heard Joe sucks". If the people strongly defended Joe, he was probably pretty good. If nobody stuck up for him, Joe might indeed suck.

Probably everyone would be silent as well if someone said "Steve Jobs sucks". This anecdote is meaningless TBH.

AR(1) models are commonly employed to model time series, and the "same-as-yesterday" model is the case where the AR1 coefficient equals 1. There is is some mean reversion in seasonally-adjusted temperature, so an AR1 coefficient less than 1 should work better.

What about Benjamin Franklin's moving weather?

Just showing a customer's previously viewed items accounted for 85% of the "lift" credited to the recommendations team I worked on.

>>The best performing variant happened to be random.

Some years ago I heard an anecdote from a developer who had worked on a video game about American football. The gist of it was that they had tested various sophisticated systems for an AI opponent to choose a possible offensive/defensive play, but the one that the players often considered the most "intelligent" was the one that simply made random decisions.

In certain domains, I think, it's quite difficult to beat the perceived performance of an AI system that merely makes committed random decisions (i.e. carried out over time) within a set of reasonable choices. If we don’t understand what an agent is doing, we often assume that there is some devious and subtle purpose behind its actions.

Humans are great at finding "patterns" in random noise.

Sometimes being unpredictable makes for a good strategy

We are very good at finding patterns where there aren't any but it's important to remember that random is actually the best answer a lot of the times. Maybe it's as simple as random plays being the hardest to predict and that's the best until you can get an AI trained on the meta of how to plan for what the current player is thinking you'll do.

It's quite common in games for AI to pick a random decision. Simply put, a good AI is a character/NPC that appears to have a mind of its own, and its own life. Nothing beats random at explaining someone's behaviour based on a personal history you don't know.

If AI responded/acted based on a predefined set of patterns that could be recognizable, the player would automatically feel it (pattern matching) and makes the NPC far less interesting.

Right-- but often an NPC is merely scenery or traffic, in the sense that their behavior does not compete with your interests. What I found interesting about the football example is that the random strategy of the NPC-oach both suggested a deeper intelligence AND proved to be an apparently effective opponent.

Beyond reinforcing our tendency to project, as you say, a personal history on random behavior, it also highlights what a few other people have commented: that in many non-cooperative situations a committed random strategy is extremely effective, and perhaps more effective than a biased, seemingly "rational" strategy. (For another example, I believe Henrich's "The Secret Of Our Success" discusses the possible adaptive benefits of divination as a generator for random strategies among early societies.)

It's how in Rock Paper Scissors you can probably do better against a smart opponent by playing randomly than by trying to trick them. At least they can't get in your head, because there's nothing there. You won't do better than chance, but at least you can't do much worse.

So, use a random strategy and measure the entropy of your opponent's behavior. And modify accordingly, as needed.

A lot of the best ml right now is effectively about making better conditional probability distributions. You always get random output, but skewed according to the circumstances, and sharp according to confidence in the result.

Unless you consider that randomness, whatever source, is some form of intelligence. (I am only half-joking here).

I'm not sure what the term for it is, but humans have an uncanny ability to ascribe meaning to pure randomness. I'm not surprised a random AI can appear smart.

When your audio player picks these random tracks https://i.imgur.com/QRoGQRy.png (example from earlier in the day) you start believing in a Higher Power..

It is well known that tit-for-tat is the best strategy for iterated prisoners' dilemma. See https://en.wikipedia.org/wiki/Tit_for_tat

It may sound similar but offensive vs. defensive behavior in (video) game strategy is a much different concept than cooperation in game theory.

I had a similar experience at one point. A team put energy into building a recommendation system, and were able to demonstrate that the "Recommended for you" content performed better than all other content editorial. After getting challenged a bit, though, turns anything performs better when put under the header "Recommended for you."

> put energy put energy

And they say English doesn't have reduplication!

Well, that’s a good lesson to learn, just put everything under “recommended for you” header! :)

You aren’t talking about dcom are you?

I worked at a larger services marketplace, helping data scientists get their models into production as A/B experiments. We had an interesting and related challenge in our search ranking algorithms: we wanted to rank order results by the predicted lifetime value of establishing a relationship between searcher and each potential service provider. In our case, a 1% increase in LTV from one of these experiments would be...big. Really big.

Improving performance of these ranking models was notoriously difficult. 50% of the experiments we'd run would show no statistically significant change, or would even decrease performance. Another 40% or so would improve one funnel KPI, but decrease another, leading to no net improvement in $$. Only 10% or so of experiments would actually show a marginal improvement to cohort LTV.

I'm not sure how much of this is actually "there's very little marginal value to be gained here" versus lack of rigor and a cohesive approach to modeling. The data scientists were very good at what they do, but ownership of models frequently changed hands, and documentation and reporting about what experiments had previously been tried was almost non-existent.

All that to say, productizing ML/AI is very time- and resource-intensive, and it's not always clear why something did/didn't work. It also requires a lot of supporting infrastructure and a data platform that most startups would balk at the cost of.

If you have historical data to validate against, you can set a leader board on models run against older data, and always leave part of the data out and unavailable for test

https://gluebenchmark.com/leaderboard/

This encourages a simple first version and incremental complexity, rather than starting very complex 6 months in, and never having an easy baseline to compare to. A simple baseline can spawn off several creative methods of improvement to research.

The other case is that the models should be run against simple cases that are easy to understand and easy to confirm. This way there's always a human QA component available to make sure results are sensible.

IMH (and biased) O, a lot of great coders are implimentors, or let's say applied computers scientists.

That is great for building incredible open source software and a lot of other things that I would not be able to do given a 1000 years. However (again IMHBO) a specific ML and any other specific application of stastistics or mathematics becomes really tricky once your use case is explicitly defined.

You then need intimate and deep knowledge of the tools that you are using (e.g.: Should I even use NN? Should I even use genetic algorithms? Should I even use x?) but ML for most people is shorthand for NN and its variants or maybe shorthand for something else specific rather than in principle.

A well aimed shot at PCA [1] can often solve your problem. Or at least, tell you what the problem looks like. This is just an example, but IMHBO people waste their time learning ML and not learning mathematics and statistics.

IMHBO I still think that self-driving cars can be solved by defining a list of 1000 or so rules, by hand, by humans, and by consensus. The computer vision part is the ML part.

[1] https://en.wikipedia.org/wiki/Principal_component_analysis

I could not agree more about self driving cars. They will disrupt and cause us to actually look at our terrible transportation infastructure, not learn to survive in it.

I wonder if this could be a case of mismatch between what the recommendations system was designed to do and what the business actually needed it to do. Your team evaluated the models based on live KPIs in an A/B testing environment, but did the recommendations team develop the system specifically with those KPIs in mind? Did they ever have access to adequate information to truly solve the problem your team needed solved? And was the same result observed for other uses of their recommendation systems?

> did the recommendations team develop the system specifically with those KPIs in mind?

Yes they did - in fact they had input on defining them and helped in tracking them.

> Did they ever have access to adequate information to truly solve the problem your team needed solved?

They believed so. Their team was also responsible for our company data warehousing so they knew even better than me what data was available. Basically any piece of data that could be available they had access to.

> And was the same result observed for other uses of their recommendation systems?

I did not have first-hand access to the results of their use in other recommendation contexts. As I mentioned in my original post I only had second-hand accounts from other teams that went the same route. They reported similar results to me.

Some ideas seem to attract smart people like moths to a flame.

It seems like everyone who joins my company to shake things up follows the same path of wanting personalized content to acquire new customers.

But in reality we just don't have enough data points on people before they become customers to segment people that way. Even if we could, being able to accurately

Every time I see people go through the motions of attempting to implement this until they eventually give up.

This idea looks like an obvious win and big companies have done them before with success, but is extremely hard to impossible to pull off for our small company.

That's surprising to hear. Comparing model performance to a randomized baseline model is a "must-have" on my team before we feel comfortable presenting to management.

An old team I advised for a while also compared model performance to a randomised baseline model.

What they didn't seem to get however was that a randomised baseline model would beat a randomised baseline model on a naive comparison 50% of the time, so their understanding of randomness/statistical significance/performance metrics was way off. So while they believed they were also testing their models before presenting to management, none of them were implementing their comparison/measurements properly, and huge parts of their work were just p- hacking and pulling random high performing results out of the tails of the many models they built and compared.

So while it's good your team makes comparison to baselines (it's alarming how many don't even do that), my experience also suggests a huge number who think they're comparing to reasonable baselines and using metrics to measure their performance aren't actually doing so properly.

I am confused, if a new model beats randomly selected randomised model 100% of time for each experiment why does it matter if randomised model beats other randomised models? Are they only comparing against the subset of worst randomised models?

I think he's saying something like the following:

1/ the team implemeted a naive baseline

2/ they implemeted a more sophisticated model that depended on some parameter p

3/ for 100 different values of p, they examined its performance, and picked the model with the best performance

Now they're not quite subject to the multiple comparisons problem there, since the models with different values of p aren't independent from one another. But they're not not suffering from it either. It mostly depends on the model. But it's a very easy mistake to make. I'd say many many academic papers make the same mistake.

Short answer: if you do it right, it doesn't matter.

Long answer: I have saying in statistics: "nature abhors two numbers: 0 and 100". In the real world, there is no 100%, you have a number of models and a (finite) number of trials/comparisons to whatever metric and then you have to then make a decision.

My point was that their "non randomised" models may in fact have the equivalent performance of a random model, and that if this was in fact the case, you would expect them to beat a randomised comparison roughly half the time. If you have repeated trials of multiple models, the odds of one consistently beating others (even if it's properties were essentially equivalent to a random model) in a small finite number of trials is much higher than most people realise. Essentially, they're flipping a large number of coins to determine their performance, and choosing the coins that consistently come up heads.

Another observation I'd make is that in the real world, random or averages are almost the most facetious thing to be comparing performance against. We aren't generally in a state ignorance or randomness, but you see this kind of metric all the time, even from "respected" sources. 2 if/then/else statements will generally outperform randomness universally in a huge number of fields/subject matter areas.

What's not interesting is that one can build a robot that beats/meets the average human at tennis (the average human probably is probably incapable of serving out a single game), but that one can build one that performs better than a relatively cheap implementation of our current state of knowledge of the game.

Moving from 2 if/then/else statements to an n parameter complicated model that requires training data and that no one understands and requires huge amounts of power and time to train is not only not progression, it's actually a regression on the current state of affairs. In almost all fields, random or average is the last thing you want to compare against.

Getting a team to publish their results (after patenting) is also a good way to get them to do these sorts of things. Significance, baselines, and other things are asked for by reviewers for the better journals and conferences.

Recommender systems are notoriously hard because it's difficult to do better than just recommending the most popular content. You can recommend more personalized content at the expense of KPIs like click-through rate.

If the team can’t even beat random, then I think that says more about your team (or perhaps your features) than about ML as a whole.

Today ML can solve some problems. In the future it might solve some problems with advances in the field. Yet other problems will likely remain unsolved, such as the stock market, or the weather, or predicting /dev/rand

"Up Next" problem can easily fall into any of the three buckets.

YouTube's "Up Next" recommendations do (significantly) better than random, therefore "Today ML can solve some problems".

>YouTube's "Up Next" recommendations do (significantly) better than random, therefore "Today ML can solve some problems".

IMO YT AI is the opposite of intelligent , it still recommends things I disliked. for some reason this basic rule of not showing something that I explicitly disliked was to hard for it to learn, I am wondering if it is truly an AI behind it or just statistics

It's intentional. Controversy is a strong signal for the youtube algorithm.

I don't think so, the videos I was referring were music videos. I engaged with it in a way but hitting dislike on a music genre I don't want to listen(I normally don't dislike things because is not my genre) in the hope the algorithm will learn but it was even worse, it did not learned that I disliked artist X and genre Y it continue to play the exact video that I disliked,

The bad algorithm will force the unhappy user to use manually created playlists leaving less people to engage with the algorithm and probably have the algorithm getting worse in time as more users will avoid it

And even more interesting trying to google "how to make youtube not show X" it is a complete fail, it will just show you youtube video results.

If the goal is simply to maximize engagement, there is no hard requirement that the algorithm should never show you things that you dislike. Essentially, what I am saying is that your belief of what their objective function is may be different from their actual objective function, and that is in no way an indication that their model is a failure.

Isn't AI statistics?

The modern AI/ML is more like we throw a lot of data and we generate a model, we know that it works but we have no idea how and why.

I'm pretty sure it does better than 'next video = random(from all of YouTube)' would, but would it be much better than 'next video = random(videos with the same subject or tags as the one playing now)'?

Yes. They've published quite a number of papers on their recommendation algorithms. You can take a look and decide for yourself whether it's snake oil or not.

Or not enough communication with the team, discussing what the objectives are, providing them with good, enough, relevant data to work with etc. I guess in many cases a data science team is expected to just "do their magic", build the AI and then come back and meanwhile not bother anyone else. In other cases, nobody really cares anyway, they just want the buzzword label to be includable in the brochures.

A relevant Twitter thread: https://twitter.com/NeuroStats/status/1192679554306887681

At the risk of projecting, this has the hallmark of bad experimental design. The best experiments are designed to determine which of many theories better account for what we observe.

(When I write "you" or "your" below, I don't mean YOU specifically, but anyone designing the kind of experiment you describe.)

One model of gravity says the postition/time curve of a ball dropped from a height should look like X. Another model of gravty says it should look like Y.

You drop many balls, plot their position/time, and see which of the two models' curves match what you observe. The goal isn't to get the curve; the goal is to decide which model is a better picture of our universe. If the plotted curve looks kinda-sorta like X but NOTHING like Y, you've at least learned that Y is not a good model.

What models/theories of customer behavior were your experiments designed to distinguish between? My guess is "none" because someone thinking about the problem scientifically would start with a single experiment whose results are maximally dispositive and go from there. They wouldn't spend a bunch of time up-front designing 12 distinct experiments.

So it wasn't really an experiment in the scientific sense, but rather a kind of random optimization exercise: do 12 somewhat-less-than-random things and see which, if any, improve the metrics we care about.

Random observations aren't bad, but you'd do them when you're trying to build a model, not when you're trying to determine to what extent a model corresponds with reality.

For example, are there any dimensions along which the 12 variants ARE distinguishable from one another? That might point the way to learning something interesting and actionable about your customers.

Did the team treat the random algorithm as the control? Well, if you believe some of your customers are engaged by novelty then maybe random is maximally novel (or at least equivalently novel), and so it's not really a control.

What about negative experiments, i.e., recommendations your current model would predict have a NEGATIVE impact on your KPIs? If those experiments DON't produce a negative impact then you've learned that some combination of the following is the case:

   1. The current customer model is inaccurate
   2. The model is accurate but the KPIs don't measure what you believe they do (test validity)
   3. The KPIs measure what you believe they do but the instrumentation is broken

Some examples of NEGATIVE experiments:

What if you always recommend a video that consists of nothing but 90 minutes of static?

What if you always recommend the video a user just watched?

What if you recommend the Nth prior video a user watched, creating a recommendation cycle?

Imagine if THOSE experiments didn't impact the KPIs, either. In that universe, you'd expect the outcome you observed with your 12 ML experiments.

In fact, after observering 12 distinct ML models give indistingiushable results, I'd be seriously wondering if my analytics infrastructure was broken and/or whether KPIs measured what we thought they did.

This is a very good comment. Is this line of reasoning fleshed out and written up somewhere so I could point people to it? (Also, I would like to think more deeply about its implications)

> What models/theories of customer behavior were your experiments designed to distinguish between? My guess is "none" because someone thinking about the problem scientifically would start with a single experiment whose results are maximally dispositive and go from there.

This is how science is (at least, ought to be) done. This way, the goal is to always be improving your understanding of objective reality.

> They wouldn't spend a bunch of time up-front designing 12 distinct experiments. [...] So it wasn't really an experiment in the scientific sense, but rather a kind of random optimization exercise: do 12 somewhat-less-than-random things and see which, if any, improve the metrics we care about.

The problem is that a lot of AI salesmen tend to hype the "model-free" nature of "predictive" AI towards optimizing outcomes/goals, and people who don't know better get carried away with the bandwagon. Overly business-oriented people are susceptible to the ostrich mentality of not wanting to understand problems with bad tools -- they are too focused on the possibility of optimizing money-making. I find the movie "The big short" to be a fantastic illustration of this psychology.

It's probably going to lead to a very bad hangover, but for the moment the party's still going on and nobody likes the punch bowl being yanked away.

Nope, I just typed the above off-the-cuff. I could tweet storm it. Would that be useful?

Personal recommendation systems all have tradeoffs. It's just the nature of curation as an intangible endeavor. You can love "Scarface", "Heat" and "LA Confidential" but still find "Casino" boring ;)

More on such tradeoffs in a recent case study from DeepMind on Google Play Store app recommendations. Even they acknowledge the same techniques that surface 30% cost efficiencies in data center cooling, may not be completely applicable to "taste"

https://deepmind.com/blog/article/Advanced-machine-learning-...

Isn't sparse recommendation for videos kind of solved in netflix prize, where the winner uses SVD to extract signature characteristic and recommend videos base on that?

Probably not. I say this because for many months, i would visit netflix and not want to watch anything. Eventually I cancelled my subscription after many years.

I think I'd rather have a random collection of titles than a recommended list for me.

There are a lot of ways of formalizing the problem of recommendation. Perhaps the variant of the problem used by Netflix is "solved", but it's kind of an odd one. Basically, they built a system to answer questions of the following form: "Given that user X watched media Y, what rating would they give it?" They trained and tested on media that users have already rated. Some of the ratings are masked and thus need to be "predicted" for the test.

The issue is that the Netflix dataset has a baked-in assumption that a recommender system should show media that a user is likely to have ranked highly. It may be more important to show the user media they wouldn't have found (and thus ranked) at all. Or perhaps a user will be more engaged with something controversial rather than generically acceptable. Who knows?

Just because the medium is the same doesn't mean the customer wants the same types of recommendations in two different contexts.

I feel like if the Netflix Prize results had truly solved the problem, then they’d still be using them. It seems like the video recommendations aren’t as good as they were ten years ago during the prize competition, and they’re no longer based on what I might like but rather what Netflix wants me to watch.

Interestingly, Steam's recommendation algorithm to show "similar to a game" by their 'learning machines' works very well. I have found really good games via that, and none of them did show up in the regular recommendations/carousel on top.

pretty sure it just uses the tags players give it, strategy, sci-fi , openworld etc.

excuse me for butting into dead conversation, but your team used ml to inapplicable case. (at least they aсquired experiense).

up next recommendations wont work without advanced image recognition and topics gathering - basically titles/tags for most videos are garbage and clickbait, and most of youtube work by watching buzzed videos: some well known (by a big amout of watchers) "influencers" push video of some topic (thing/brand) then it get traction from other content creators - they produce videos about it and watchers tend to stick to buzzed topics. it's like news about news.

if your team used ml to recommend up next on your own video hosting your result simply means your videos are equally not on topic Or non-interesting for your service auditory; or they are garbage.

Doesn’t it depend on the size and variety of your dataset?

I can see “random” performing well in a set of <1000 videos, all on similar subject spaces (eg “memes”, or “python”), but recommending relevant stuff gets much harder as the amount of content grows...

I feel like A/B testing isn’t a great way to determine correctness either.

IMO even in interface designing you should be arguing from first principles rather than relying on telemetry and other empirical data.

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