41–50 of 205 posts
I’d enjoy seeing a more creative approach to recommendations for something as big as Netflix. A few suggestions: 1. Review channels by genre 2. Trailer TV - let me leave a “comedy” trailer channel running that shows the trailer and movie rating and details at the bottom, let me easily skip to the next trailer (or let it play out)
I'd just like to tell Netflix I want to go to bed in 40 minutes so play something about that long.
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?
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He worked 10 years there, and was former research director, so.. maybe he knows a bit or two about this prize? He did work on productionize the 2007 winner, after all.
His LinkedIn profile says otherwise. 2011-2017 was his time at Netflix. https://www.linkedin.com/in/xamatriain
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> At the time, Netflix wasn't super high-tech I will admit that it was interesting to see what algorithms were poised to be cutting edge in media recommendation. The result was rather disappointing to me. Netflix STILL isn't that exciting from anything but a compensation standpoint. The problems at netflix are about programming, while the technical challenges are droll at best.
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,…
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,…
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.
Earlier quoted context omitted.
> At the time, Netflix wasn't super high-tech I will admit that it was interesting to see what algorithms were poised to be cutting edge in media recommendation. The result was rather disappointing to me. Netflix STILL isn't that exciting from anything but a compensation standpoint. The problems at netflix are about programming, while the technical challenges are droll at best.
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,…
I’d enjoy seeing a more creative approach to recommendations for something as big as Netflix. A few suggestions: 1. Review channels by genre 2. Trailer TV - let me leave a “comedy” trailer channel running that shows the trailer and movie rating and details at the bottom, let me easily skip to the next trailer (or let it play out)
Anything can beat their current algorithm of "push people towards our originals, preferences be damned". EDIT: I should add in terms of customer satisfaction, not revenue. I am sure forcing their originals down people's throats is great for their revenues.
Earlier quoted context omitted.
> At the time, Netflix wasn't super high-tech I will admit that it was interesting to see what algorithms were poised to be cutting edge in media recommendation. The result was rather disappointing to me. Netflix STILL isn't that exciting from anything but a compensation standpoint. The problems at netflix are about programming, while the technical challenges are droll at best.
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,…
Earlier quoted context omitted.
> At the time, Netflix wasn't super high-tech I will admit that it was interesting to see what algorithms were poised to be cutting edge in media recommendation. The result was rather disappointing to me. Netflix STILL isn't that exciting from anything but a compensation standpoint. The problems at netflix are about programming, while the technical challenges are droll at best.
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,…
Now a days, I'm certain Netflix recommends content to feature either "no cost" (owned) or the content with the lowest licensing fee. I don't believe for a second they don't have the data suggest the best movie. They simply don't want to suggest the best movie. As you said, their goal (now) isn't to suggest the content the user is likely to enjoy most, it's to suggest content the user will tolerate. And that's exactly why they shifted away from a 5 star rating system, to a thumbs up/down approach... even if you didn't love a movie or show, you're still likely to give it a thumbs up unless it was totally awful.
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,…
In Mark Randolph's book he talks about how Netflix would recommend content (DVDs at the time) to strategically fit Netflix's needs. For example, if they didn't have a copy of a movie ready to send out, Netflix wouldn't recommend it. Now a days, I'm certain Netflix recommends content to feature either "no cost" (owned) or the content with the lowest licensing fee. I don't believe for a second they don't have the data…
Large numbers of books labelled as 'free with your membership', which likely only cost Amazon the price of delivering the files. Which makes sense, because once I have paid for my credit the worst outcome financially is that I use it.