Show HN: A 2D map of the 1000 most popular books on HN
8 points| pmaze | 1 year ago |hnbooks.pieterma.es
- OpenAI's embeddings were processed using UMAP and HDBSCAN. A direct 2D projection from the text embeddings didn't yield visually interesting results. Instead, HDBSCAN is first applied on a high-dimensional projection. Those cluster memberships are then embedded using UMAP again, which results in the dense structures I wanted.
- There are multiple books with the same title. Currently, only the most popular one of those makes it onto the map.
- The books' descriptions are based on extractions from individual posts and GPT's general knowledge. Quality levels vary, and it leads to some oddly specific points, but I haven't found any yet that are straight up wrong.
- It's surprisingly hard to get good quality book cover images! I tried Google Books and a bunch of open APIs, but they all had their issues. In the end, I got the covers from GoodReads through a hacked together process that combines their autocomplete search with GPT for data linkage.
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