[greatthanks]

I am absolutely not at all whatsoever impressed with Netflix' data science capabilities.

They waste one row for:

- watch it again ...

- they suggest stuff based on what I added to my list, but didn't watch yet

- they suggest movies based on what I started to watch but didn't finish - as if that wasn't an indicator for that I didn't like it

- ...

All those companies suggesting on varies channels how nifty and smart they work with their big data - and at the bottom line they just fail on actually improving something.

[tfgg]

Do you think that those design decisions are not backed by data demonstrating better engagement levels versus other interface layouts? I'm not saying that their interface is perfect, but I don't see how you can make sweeping claims about their data science capabilities based on your one data point of disliking their interface.

[fluidcruft]

I think at some point people assume others are more competent than they actually are. I mean, honestly, here you are defending Netflix based on nothing more than stereotypes and projecting competence into them--when the linked PR story is so ridiculously methodologically flawed that a rational observer would seriously question their basic mathematical and psychological literacy.

[SyneRyder]

If you don't like Netflix's ratings recommendations, you might like to try MovieLens.org. They let you choose between different algorithms & provide some additional stats (eg your average rating per genre, unusual likes & dislikes, distribution of ratings by decade).

[JoeAltmaier]

Cool! But at some point, providing stats yourself is just fudging the algorithm. Why not just browse those categories yourself, instead of diddle an algorithm and seeing if it predicted correctly? Except maybe that's fun too.

[JoeAltmaier]

They list movies I've watched and rated! In every category. Some categories consist almost entirely of movies I've watched and rated.

Its not only unimpressive; its actually incompetent.