

A better reading list with Mathematica - momo-reina
http://blog.higher-order.com/blog/2015/02/08/a-better-reading-list-with-mathematica/

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RBerenguel
Last year I decided to use a totally random method (after reading an
interesting article linked here) to choose my next book among my huge reading
list. Was weird and fun, since it let me read things I would have put off for
a long time.

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splat
I've started using a semi-random method. I pick a category, take a random
page, and then pick whatever book I want from the twenty books on that page.
I've enjoyed it so far. It gets me to read books that I may have put off for a
while, but because I'm picking the top one out of twenty they're books that I
really want to read.

~~~
RBerenguel
I did this during that time, with iOS games. Since I work as editor for a
large-ish app review portal, I have tons of games. Some I like, some I want to
try... But too little time. So, I rolled a dice (well, a virtual one) to know
which page and which game to try when I was in a playing mood. Doing this I
"discovered" several lovely games I had hidden in my folders, I also did some
huge cleanup of games I didn't like or no longer enjoyed and discovered I like
backgammon enough to pick it up as one of my favourite board games (long
behind go, but backgammon is faster to pick up and play on mobile.)

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runj__
My reading list is also very long but I'm trying to mix on books that I think
I'll really really enjoy with books that are good for me (that I'll still
enjoy, but not as much).

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Aqwis
Is there an IMDB Top 250 list out there ranked by percentage of 10/10 ratings
instead of average rating?

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stared
In IMDb it is interesting, as you can (roughly) decompose ratings in 1s
(haters), 10s (lovers) and the rest (who actually rate it). But I would say
that the last part is the most important, unless you want to get the hype.

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novalis78
one method that works extremely well for me is to pick a book based on
Amazon's "what other customer's bought". I noticed that some of the best books
I read appear in clusters that other customers/peers valued equally high

