I also like https://shepherd.com/. One of its interesting features is that authors list their five favorite books and say why they like them.
Id love to have https://same.energy for book contents.
Really interesting that my favorite sci-fi book, Pandora's Star is in the middle of a void in the center of a large sci-fi cluster.
It explains why I couldn't find anything like it.
Also very explanatory the fact the Tolkien's The Two Towers is right by its side, because I also love that book.
And now I'm already downloading the other "outliers" close to Pandora's Star.
Anvaka's YASIV was an extremely strong tool in this space until Amazon discontinued the API it relied on.
Kind of hacky but I built something similar to apply the page rank algorithm to the authors referenced between books of various topics, here's the result for science:
https://camjohnson26.github.io/author-graph/science/
https://github.com/CamJohnson26/author-graph
Clearly needs a lot of data clean up but still was very helpful for discovering important scientists and their approximate relative impact
The visualizations remind me of those in a paper I co-authored a while ago (2009) visualizing ~2400 scientific journal / ~5.7M full-text articles: "Semantic Journal Mapping for Search Visualization in a Large Scale Article Digital Library" https://nrc-publications.canada.ca/eng/view/accepted/?id=63e...
Cool visualization, but the model needs work. The closest book to "Harry Potter and The Chamber of Secrets" is "The Victorian Lady's Guide to Sex, Marriage, and Manners".
> Only include reviews which came from users who had at least 10 reviews.
Not sure if that's a good idea. It shrinks the set of genuine readers and overweights the set of professional spammers.
This looks super cool, but why not use this tool as a non-visual tool to show similar books given a title? As far as I know there aren't many tools for this
Also it would be super cool if we could import out goodreads reading lists and see them on the cluster
This is super cool! I actually have been working on a new book recommendation site (https://braincandy.com) that has a similar-ish (but much smaller scale) visualization for book similarity. It is really interesting how certain genres tend to be much more insular than others and it can be a real challenge to break out of genre boxes when making recommendations. There's so many books out there on the edges and in-betweens that get lost when they don't fit neatly into an existing popular genre, and those indeed can be some of the most interesting.
This is awesome. I only wish the author haden't waited years after scrapping! Many books I've loved have been released in the past couple years
This is an awesome visualization, I am so impressed :). I've been working on (shepherd.com) with a similar goal to try to bring in human groupings to try to determine book connections via the many angles humans bring to the table. And more serendipity like wandering a local bookstore. I really love how you have done this.
Dropping you an email in a few hours :)
Impressive Tool. I would love to have the same for movies.
I'm surprised there are enough books published under the category of "Reverse-harem" to make it its own category along with things like "Horror", "Fantasy", "Business", etc.
Nice article. Not what I was expecting...
I was really hoping this would address "Visual Book" like in ふしぎの海のナディア, Fushigi no Umi no Nadia/The Secret of Blue Water. The LaserDiscs used to say "Visual Book" in every episode.
Great animated series (to me), a mix of 20,000 Leagues Under the Sea and the Illuminati.
Do NOT watch it with the english subs. Suffer with the Japanese, even if You don't speak it. You don't need the junk verbal translation, You will still get the main concepts. And Hanson's driving is so much more manaical in Japanese.
Did I miss how we are supposed to get recommendations from what OP built?
For post-t-SNE processing to get non-overlapping items, see also: https://github.com/Quasimondo/RasterFairy
I also used more crude algorithms that sort by X, group elements in buckets, and within each, sort by Y. Then we get a grid of elements. The result is less high-quality than with iterative algorithms (and depends on if we sort by X or Y first), but it is hard to beat its simplicity.
This is really cool!
I wish the accompanying article was longer. I can't fully grasp how it was done because I don't know enough about the concepts mentioned.
I think that people in general don't read "enough", or maybe can't "enough." By that I mean, recommendations based on other people's reading habits aren't going to be as fine tuned as say, recommendations based on people's music listening habits.
Really cool!
Reminds me a lot of some of the stuff I used to work on at Steam Labs: https://store.steampowered.com/labs
I'm astonished by how well this works. I looked for a bunch of books I'd read recently and kept finding other books I've read or want to read near them.
Love this. Bought a couple of books similar to "Midnight's Children' - author should definitely think about adding affiliate links!
This is great. I think visual tools like this are under utilized. They’re fun to use and can often reveal interesting insights.
similiar t-SNE visualisation, just for papers:
https://static.nomic.ai/pubmed.html
running on their deepscatter visualization engine:
https://github.com/nomic-ai/deepscatter
that keeps things dynamic for rendering
Have you presented this to LibraryThing?
Funny that most remote and isolated clusters ended up being m-m-romance and ... manga.
The gang of four design patterns book being two away from a MAGA book is funny.
Nice! Is there a github repo?
Made an account just to comment on this. Look incredible.
Something's wrong here. I was very excited to explore this, until I searched for "Getting Things Done" by David Allen. A nearby book - "Crippled America: How to Make America Great Again" by Donald J. Trump.
Reminds me of https://www.literature-map.com
Which is a map of all authors in the world sorted by overlap in readership. I found some of my favorite writers by browsing it.
I wonder which approach is better suited to find something that is spot on to my interests.
When I think of my favorite books, they usually are the most popular books of their authors.
Are there any counterexamples, where an author wrote a book that is more profound than their biggest hit but got overlooked for some reason?