Predicting Hacker News article success with neural networks and TensorFlow
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Re: Predicting Hacker News article success with neural networks and TensorFlow
#2Re: Predicting Hacker News article success with neural networks and TensorFlow
#3Ignoring the outlier, stories have at best a 1 in 3 chance of succeeding on HN. This means 2/3 of the interesting stories passing through HN are lost, and I could potentially triple my high quality reading material. It is amazing how much good content there is out there that I will never find.
Re: Predicting Hacker News article success with neural networks and TensorFlow
#4Interesting article. Ignoring the outlier, stories have at best a 1 in 3 chance of succeeding on HN. This means 2/3 of the interesting stories passing through HN are lost, and I could potentially triple my high quality reading material. It is amazing how much good content there is out there that I will never find.
Re: Predicting Hacker News article success with neural networks and TensorFlow
#5So: Consider the trailing space ...
Re: Predicting Hacker News article success with neural networks and TensorFlow
#6Did the author chose that title on purpose? :)
It's fun to try a title, and then add "Ask HN:" or "Show HN:" in front of it and see the probability change dramatically, or remove the (YC ...) from the extreme examples and see the prediction change.
Re: Predicting Hacker News article success with neural networks and TensorFlow
#7Re: Predicting Hacker News article success with neural networks and TensorFlow
#8Interesting article. Ignoring the outlier, stories have at best a 1 in 3 chance of succeeding on HN. This means 2/3 of the interesting stories passing through HN are lost, and I could potentially triple my high quality reading material. It is amazing how much good content there is out there that I will never find.
Think of it like when you go to a restaurant, sometimes you choose a meal but you notice other people eating things that look more interesting which you are now too full to try. It's possible you'll never been able to eat at that restaurant again (eg if you're on vacation or it's the last night of the restaurant's operation (I've had quite a lot of favorite restaurants shut down, taking many fond gastronomic and social memories with them).
But really, this is a misunderstanding of opportunity cost. Those other meals may have been delicious, but so was yours; you would still only have been able to comfortably eat one meal, rather than the whole menu; and if you had had what your neighbor was eating, then you might have regretted not having your own meal.
Game theorists and economists explore regret minimization frameworks for decision-making, and that's valuable, but you have to consider both the extra work involved in applying the framework vs the real opportunity cost. A surfeit of choice (whether via advertising or in reality) can lead to overestimation of opportunity cost by tricking you into imagining you could enjoy all alternatives whereas in reality your selection was going to be limited anyway.
you may find it interesting to think about the psychology of collecting, and how it differs from usage. Collections can themselves have considerable value (scientific, cultural etc.) but some collections are the result of acquisition gone wrong and tipping into hoarding without any enjoyment of the collected object.
Re: Predicting Hacker News article success with neural networks and TensorFlow
#9EDIT: This beats it with 99.4%:
" Rust Rust Rust Rust Rust "