
Challenges in Search on Streaming Services: Netflix Case Study - infodocket
https://arxiv.org/abs/1903.04638
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bunderbunder
Netflix is one of my favorite examples of a search service that would be
infinitely improved with a really low-tech change that will probably never get
implemented because it doesn't give the data scientists something to talk
about over beer: Filtering

So often, what I'm looking for is something like, "G rated content, not by
Disney, less than 30 minutes long", or "Documentaries that I haven't watched
yet, with an audio track in French, that are available for offline viewing".
With a search space the size of Netflix's library. There's no need for a
search string there. Nor is there a need for anything fancy like
recommendation or expanding to items that are similar to direct hits on the
search string, because, these days, a basic filter like that will only yield a
page or two of results, anyway. And would be really easy to for them to
implement, because they already have their library tagged with pretty much
anything I could think to filter on, anyway, so they can use it as features
for the fancy-schmancy recommendation engine.

More generally, I stopped having a need for a juiced up search engine and
recommendation system in Netflix the exact moment I stopped owning a DVD
player.

~~~
apostacy
Comedy Central had something like that for their shorts from The Daily Show,
back in like 2012. You could drill down with things Person:"Samantha Bee" in
place:"New Jersey" in when:2009.

It was really quite remarkable. But then the UX experts got to it, and ruined
it.

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yannyu
>and support searches for unavailable (out-of-catalog) entities

This is a subtle killer feature of Netflix's search, and something not often
done by others. By including these unavailable entities, it allows Netflix's
similarity and recommendation engines to still understand what the user was
looking for, and provide things that are relevant and similar, even if they
aren't exactly what the user is looking for.

That being said, this is a lot easier for something that has a relatively
finite, known set of items (movies) in contrast to things with lots of items
that can be differentiated in smaller, subtler ways (e-commerce, web
documents).

