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I couldn't help but notice S(h, t). Never the less it was a very good article.

Very interesting read, I wonder if Twitter does it in a similar way.

Anyone who knows where to learn more about the system design they use, with the pre-processor, parser, scorer, and ranker?

How do these interact? What's the respective input and output?

Links to some similar system with more in-depth on this area would be appreciated.

This was quite good.

Interesting that most hashtags don't get more than 3 posts per hour - that breakdown would be a good graph to see! It's also curious that they don't apply some type of seasonal decomposition to the timeseries data; though perhaps that that makes sense with Instagram's particular data.

Differentiation between popularity and novelty? What a succinct explanation. Incidentally Twitter originally used popularity, but switched to novelty[1] because Justin Bieber regularly dominated the trending tweets list.

[1]http://mashable.com/2010/05/14/twitter-improves-trending-top...

Surprisingly, I've applied most of the mentioned algorithms on real world data while working on some side projects. Realized that even big organizations use fundamental Machine Learning algorithms to get their tasks done. This feels quite reassuring as you don't need a MS/PhD to solve such complex/interesting problems.

Nice. I learned a ton with this. Definitely adding the various types of calculations to my reading list. I haven't googled for this yet, but is there an open source hashtag trends detector around? If not, would be pretty cool to use the teachings of this post to build one.

This is an awesome post! I really appreciate the level of detail they go into.

Also, I found a typo:

> ... to requests comping from the app

(I think that should be 'coming from the app')