Thanks for taking the time to compile this list, I love these sort of data-driven listings, while they have their weaknesses (as noted in some of the comments) they are still valuable in understanding what is popular.
I'd be interested to see if you collated by year if the order of the recommendations changed. One of the issues with recommended book lists is that they tend to be self-reinforcing, people read books on someone's list and then recommend them.
A number of the books on the list are quite old in coding years, while this doesn't mean they don't deserve to remain on the list...I wonder if they would trend less highly given some sort of curve for recency of recommendation?
I thought about this as I was making this list, but wasn't able to find enough recommendation lists to do any sort of slicing. You could do something similar with data from big websites with lots of reviews like Amazon. Look at the frequency and median value of reviews over time, and weight more strongly a book with the same median rating, but more overall reviews in a given time period. That's a slightly different question than the one I was trying to answer in this post, though.
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Thanks for taking the time to compile this list, I love these sort of data-driven listings, while they have their weaknesses (as noted in some of the comments) they are still valuable in understanding what is popular.
I'd be interested to see if you collated by year if the order of the recommendations changed. One of the issues with recommended book lists is that they tend to be self-reinforcing, people read books on someone's list and then recommend them.
A number of the books on the list are quite old in coding years, while this doesn't mean they don't deserve to remain on the list...I wonder if they would trend less highly given some sort of curve for recency of recommendation?
I thought about this as I was making this list, but wasn't able to find enough recommendation lists to do any sort of slicing. You could do something similar with data from big websites with lots of reviews like Amazon. Look at the frequency and median value of reviews over time, and weight more strongly a book with the same median rating, but more overall reviews in a given time period. That's a slightly different question than the one I was trying to answer in this post, though.