4 ms·
A better reading list with Mathematica
- runj__ 12y agoMy 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).
- RBerenguel 12y agoLast 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.
- splat 12y agoI'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 12y agoI 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.)
- Aqwis 12y agoIs there an IMDB Top 250 list out there ranked by percentage of 10/10 ratings instead of average rating?
- stared 12y agoIn 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.
- zawodnaya 12y agoThis may not be a very useful method for IMDB. If you follow this method you will be watching a lot of documentaries and movies made before 1970.
- novalis78 12y agoone 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