151–156 of 156 posts
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You >quoted with no commentary.. so your point is :: TROLL?
So are you just making the punctuation up as you go, or what?
Carry On with Dangs Blessing.
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This hardly seems like a reasonable way to characterize Netflix, which has a personal recommendation system, especially compared to HN, which is ad supported yet gives the same recommendations to everyone.
My reading of their comments is that they are trying to say that social media and news media can be characterised as having recommendation systems too, not just song and movie platforms (I don't know who exactly they're arguing against – I've never heard anyone say that recommendation systems can only be for songs and movies). I don't think they're really paying much attention to the dimension you're splitting it alo…
* HN and classic Reddit sort their items on a single dimension ("hotness"), calculated using a few input variables and producing a single output variable. This is about as cheap to calculate as recommendation systems get. The XKCD comment recommender is a bit more complex, but still in the same complexity class. Since the whole point of an algorithm like this is to be timely, the naive approach is to compute it on-the-fly, which it's perfectly simple enough to manage.
* At a somewhat more complex level, you get stuff like a basic, uncustomized Similar Items list. If YouTube has no data on you, this is what you get from their sidebar recommender (and their front page would be analogous to Reddit and HN, but sharded by region and language). It's also pretty close to what AdWords used to be, before they started doing user profiling. The thing with this method is, even though it involves some level of AI, it's presenting the same thing to everyone and it's expensive, so the natural solution is to precompute it.
* Personalized recommenders are the worst of both worlds. You can't naively compute it on-the-fly, because it's too slow, but you also can't naively precompute it, because there's a combinatorial explosion of users and items. You actually have to be clever about it.
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If You have 10000 posts that You have to sort it in some way and the user just going to see 20 of those, the sorting is the recommendation system, people are just used to think of products, movies and songs, but in those platforms the users are the products
This hardly seems like a reasonable way to characterize Netflix, which has a personal recommendation system, especially compared to HN, which is ad supported yet gives the same recommendations to everyone.
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YouTube has since migrated to Spanner, there’s a podcast episode with one of the Vitess creators that covers the politics of the switch
That sounds interesting — do you have a link?
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Wow, this is something that has been a floater-in-mind for decades ; I'll top it off with an interview at Twitter with the Eng MGR ~2009-ish? -- Him: So tell me how you would do things differnetly here at twitter based n your experience? ME: " Well, I have no idea what your internal processes are, or architecture, or problems, so my previous experience wouldn't be relevant. " I'd go for the best option that suits goa…
Wow. That amazes me that anyone would answer that question without knowing anything about the problem space and implemented solutions. Wait, I got it, I would rewrite everything as AWS Lambdas. That's the right answer! Screw your (almost certainly SQL) DB, let's move it all to DynamoDB too.
I was stating that the eng mgr was relieved to NOT hear an answer of "the way we did it at company X, and it was successful for them, so I assume that the same approach maps to your company"
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Are we talking about the same thing?
With all of this technology applied, I am still disappointed by Netflix's recommendations – to the point of just giving up and doing something else.
Do you think part of this is that Netflix has assumed zero effort from user model? My experience has been that Netflix does an ok job of recommendations, but fails at overall discovery experience. There is no way for me to drive or view content from different angles easily. I end up googling for expert opinions or hitting up rotten tomatoes to get better reviews. Netflix knows a ton about me and their content, but se…