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How useful was the Netflix Prize challenge for Netflix?

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Re: How useful was the Netflix Prize challenge for Netflix?

#81

Post by EnderWT »

kens wrote: post #51
BB212 wrote: post #20

I think the discussion misses the most important part: The goal of the Netflix prize wasn't to come up with the best algorithm - it was to make the Netflix brand exciting and legitimate to engineers. At the time, Netflix wasn't super high-tech and I'm sure it was hard for them to get the top talent they needed. It seems silly in retrospect now, but I'm certain the reason this was approved was because they wanted the…

> make the Netflix brand exciting and legitimate to engineers As a serious question, why do people include Netflix in the acronym FAANG, which I see on HN all the time? Is there something special about Netflix? Netflix is around the #14 tech company, so it's strange to see Netflix in there instead of Microsoft. Or is the use of FAANG divorced from its literal meaning?

Jim Cramer and Bob Lang coined FANG back in 2013 based on this criteria:

"Put money to work in the companies that represent the future," he said. "Put money to work in companies that are totally dominant in their markets, and put money to work in stocks that have serious momentum."

https://www.cnbc.com/id/100436754

Re: How useful was the Netflix Prize challenge for Netflix?

#82

Post by koolba »

ma2rten wrote: post #62
derangedHorse wrote: post #59

Earlier quoted context omitted.

It's probably included because of the sky-high salaries they offer since FAANG is typically an acronym used to refer to top software companies to work for. From what I've heard from friends, Microsoft typically pays the least out of all the companies that make up the acronym and their technologies are also seen as less trendy than the other companies listed.

That is not the origin of the term. It was coined by Jim Cramer for fast growing stocks.

And before that he had his “four horsemen”: https://www.barrons.com/articles/BL-TB-5933

(Note this article is from Jan of 2008): > In today's trading, all four stocks are down steeply: Apple: Down $26.65, or 17.1%, to $128.99. Amazon: Down $7.56, or 9.6%, to $70.92. Google: Down $48.15, or 8.2%, to $536.20. Research In Motion: Down $8.47, or 9.4%, to $81.61.

Even writing off RIMM to zero would give you a healthy return through 2021.

Re: How useful was the Netflix Prize challenge for Netflix?

#83

Post by bawolff »

makeitdouble wrote: post #40
rrrrrrrrrrrryan wrote: post #31

Earlier quoted context omitted.

Who's more likely to keep renewing their subscription? The person who uses Netflix to watch a ton of trash that they think is "just ok," or the person who merely watches 1 or 2 things per month that they actually enjoy? I'm certain Netflix ran the numbers, and determined that a high-usage customer is the most valuable.

It's interesting how many corporations don't actually "run the numbers" on what we think are important issues. Basically, internal focus and what the rest of the world cares about are disjointed and corps are often blind to obvious aspects. This can be improved by strong internal diversity, but Netflix doesn't look like a bastion of that (yet?) On "just ok" vs stuff actually enjoyable, "just ok" is fine until there i…

Wouldn't this be a short term vs long term optimization thing? In the short term "just ok" wins. In the long term, users might get bored and new users are less likely to join. Or at least that's my gut feeling, i have nothing to back it up.

Re: How useful was the Netflix Prize challenge for Netflix?

#84

Post by kqr »

danielharan wrote: post #53

I learned about AI by participating in the challenge, and ended up co-founding an AI company as a result. Netflix got more than 1 million dollar in free advertising from it, and are still getting brand value out of it today. They implemented some of the algorithms, and probably got a 10X ROI through retention alone. As mentioned in the Quora answer, they were also able to recruit top talent - and that's much harder t…

Similar story here. The company I work at founded when a couple of very bright people realised why the Netflix stuff won't generalise to many other areas, and sought to find something that did instead. So far, very successful!

Re: How useful was the Netflix Prize challenge for Netflix?

#85

Post by raverbashing »

_pastel wrote: post #66
uyt wrote: post #42

> are open algorithmic contests useful and valuable? Kaggle has been around for a long time now. If it works, I would expect them to be pumping out tons of interesting results from winners but I don't think I've heard many stories like that. It seems to be mostly useful for recruiting purposes?

Kaggle competitions rarely produce interesting algorithmic results. But I highly encourage you to read the winners' solutions. They are full of clever data insight, augmentations, regularizations, feature engineering, and preprocessing and postprocessing tricks. But above all, compared to the academic literature, it's shocking how much time and creativity they spend on validation . Maybe I'm reading the wrong papers,…

> But I highly encourage you to read the winners' solutions. They are full of clever data insight, augmentations, regularizations, feature engineering, and preprocessing and postprocessing tricks.

> But above all, compared to the academic literature, it's shocking how much time and creativity they spend on validation. Maybe I'm reading the wrong papers, but the flashy new neural architectures rarely even mention their validation setup; Kaggle winners sometimes devote half of their explanation to it

I agree, but in the end it is a competition, and the solution that scores the most is not always the solution that is "the most interesting" (or practical, or best in real world cases)

Though the details you mention are interesting, and can definitely apply at real-life solutions.

Re: How useful was the Netflix Prize challenge for Netflix?

#86

Post by __vim__ »

calderwoodra wrote: post #80

I had the pleasure of working closely with Yehuda (one of the earlier prize winners) at Google where he works on TV and Movie recommendations (Search "what to watch" to see his team's work). He's extremely intelligent and passionate about this space, and every time we spoke I felt like I was learning something new. You can listen to him give an in depth talk about the Netflix problem and solution here [1]. [1] https:…

Thanks. Will watch later

Re: How useful was the Netflix Prize challenge for Netflix?

#87

Post by setr »

Jarwain wrote: post #48
setr wrote: post #28

Earlier quoted context omitted.

IMO the recommendations are no good because they fundamentally take the wrong approach — rather than ask the user what they like, they try to guess what you like based on usage (which really doesn't correlate well — I watch a lot of garbage because I can’t find things I like, and I don’t have anything better to do.) And they don’t ask because users don’t provide useful answers. But users don’t provide useful answers,…

Your rating system thing kinda makes me think of a cross between goodreads and myanimelist. All that's left is making it convenient to rate & review

MAL was actually my source for thinking on this subject. In combination with the book Otaku: Database Animals[0] (anime fans catalogue the hell out of things, and this extends to tracking their anime and ratings) I realized you should be able to put together some very strong recommendations by scraping the MAL dataset — because the data should be fairly honest.

And then the realization that really the best recommendation isn’t to forge a new customized list altogether — it’s to simply find the most similar users and recommend items from their list. (MAL has/had a cosine similarity function for this, but no way to search because it’s basically an n^2 algorithm on 4M users; apparently they offered it at some point, and quickly found it untenable. That was what really kicked me off)

And then the realization that if I found users with similar taste, then shouldn’t they be friends? So then it becomes a MAL friendship algorithm..

Did a bunch of research on recommendation algorithms and weighting strategies, scraped most of the MAL users, stored it in a database, and then promptly procrastinated on actually implementing the algorithms. Been sitting on that for like 3 years now :|

[0] https://www.amazon.com/Otaku-Database-Animals-Hiroki-Azuma/d...

Re: How useful was the Netflix Prize challenge for Netflix?

#89

Post by moltenguardian »

kens wrote: post #51
BB212 wrote: post #20

I think the discussion misses the most important part: The goal of the Netflix prize wasn't to come up with the best algorithm - it was to make the Netflix brand exciting and legitimate to engineers. At the time, Netflix wasn't super high-tech and I'm sure it was hard for them to get the top talent they needed. It seems silly in retrospect now, but I'm certain the reason this was approved was because they wanted the…

> make the Netflix brand exciting and legitimate to engineers As a serious question, why do people include Netflix in the acronym FAANG, which I see on HN all the time? Is there something special about Netflix? Netflix is around the #14 tech company, so it's strange to see Netflix in there instead of Microsoft. Or is the use of FAANG divorced from its literal meaning?

Up until the beginning of 2020, NFLX was the highest growing stock of the decade. (Dethroned by TSLA) In terms of percentage growth I believe it still outperforms every other component of FAANG.

Re: How useful was the Netflix Prize challenge for Netflix?

#90

Post by doublescoop »

thom wrote: post #88

Do people feel that Netflix has a large enough catalog that its recommendation system really matters? The only useful feature it ever had for me was the ‘new this week’ category that seems to have been retired.

The company reportedly had 100,000 DVD titles in their catalog, so yeah. Remember how old this contest was.
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