Show HN: The Sample – newsletters curated for you with machine learning

1

FastText

I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery[1]; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib[2]. It's pretty easy to play around with, just give it a csv of <user id>, <item id>, <rating> and then you can start making rating predictions. Also fastText[3] is easy to mess around with too. Most of the code I've written just layers things on top of that, e.g. To handle exploration-vs-exploitation as discussed in another thread here. Recently I've been factoring out the ML code into a separate recommendation service so it can different kinds of apps (I just barely made this essay recommender system[4] start using it for example). I'm happy to chat about recommender systems also if you like, email's in my profile. [1] https://findka.com [2] http://surpriselib.com/ [3] https://fasttext.cc/ [4] https://essays.findka.com.

#Natural Language Processing #Spreadsheets #NLP And Text Analytics 4 social mentions

2

Findka

I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery[1]; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib[2]. It's pretty easy to play around with, just give it a csv of <user id>, <item id>, <rating> and then you can start making rating predictions. Also fastText[3] is easy to mess around with too. Most of the code I've written just layers things on top of that, e.g. To handle exploration-vs-exploitation as discussed in another thread here. Recently I've been factoring out the ML code into a separate recommendation service so it can different kinds of apps (I just barely made this essay recommender system[4] start using it for example). I'm happy to chat about recommender systems also if you like, email's in my profile. [1] https://findka.com [2] http://surpriselib.com/ [3] https://fasttext.cc/ [4] https://essays.findka.com.

#eCommerce #Directory #eCommerce Tools 2 social mentions

3

The Factual

> Moreover investment of time on user side for newsletter delivering news increases with each newsletter vs One newsletter delivering what I want. You might be interested in this article[1] and the discussion of subscribed vs. Filtered sources. It'd be nice to have a convenient way to assign different priorities to newsletters/content sources. So for a few, you'd get every issue, and for the rest, they'd be combined together in some way. > You're welcomed to join the discussion on 'Get me the news I need, not the news I want' by explaining what Sample does to address that problem. I'm not sure I have much insight about needs vs. wants, but you might be interested in The Factual.[2] [1] https://jacobobryant.com/p/the-pros-and-cons-of-algorithmic/ [2] https://thefactual.com/.

#News #Chrome Extensions #Safari Extensions 5 social mentions