clarity, typos

1 files changed, 13 insertions, 12 deletions

diff --git a/content/articles/2022-11-10-artist-correlation.md b/content/articles/2022-11-10-artist-correlation.md
index c8eaa62..c62e790 100644
--- a/content/articles/2022-11-10-artist-correlation.md
+++ b/content/articles/2022-11-10-artist-correlation.md
@@ -2,23 +2,24 @@
 
 A hear a lot of music and so every few months my music collection gets boring
 again. So far I have asked friends to recommend me music but I am running out of
-friend too now. Therefore I came up with a new solution.
+friend too now. Therefore I came up with a new solution during a few days.
 
 I want to find new music that i might like too. After some research I found that
-there is [Musicbrainz](https://musichbrainz.org/) (a database of all artists and
+there is [Musicbrainz](https://musicbrainz.org/) (a database of all artists and
 recordings ever made) and [Listenbrainz](https://listenbrainz.org/) (a service
-to which you can submit what you are listening too). Both databases are useful
+to which you can submit what you are listening to). Both databases are useful
 for this project. The high-level goal is to know, what people that have a lot of
-listens in common, like to listen to.
+listens in common, like to listen to. For that the shared number of listeners
+for each artist is relevant.
 
 ## The Procedure
 
 ### Parse data & drop unnecessary detail
 
 I parse all of the JSON files of listenbrainz and only keep information about
-how often what user listens to which artists. The result is stored in a B-tree
-map on my disk (the [sled library](https://crates.io/crates/sledg) is great for
-that).
+how many listens each user has submitted for what artist. The result is stored
+in a B-tree map on my disk (the [sled library](https://crates.io/crates/sledg)
+is great for that).
 
 - First mapping created: `(user, artist) -> shared listens`.
 - (Also created a name lookup: `artist -> artist name`)
@@ -30,17 +31,17 @@ a user, by scanning the prefix `(user, …`.
 
 Next an undirected graph with weighted edges is generated where nodes are
 artists and edges are shared listens. For every user, each pair of artists they
-listen to, receives the sum of listens to either one's listens.
+listen to, receives the sum of listens to either one's listens. This means that
+artists that share listeners are connected.
 
-Mapping: `(artist, artist) -> weight`.
-
-Every key `(x, y)` is identical with `(y, x)` so that edges are undirectional.
+Mapping: `(artist, artist) -> weight`. (Every key `(x, y)` is identical with
+`(y, x)` so that edges are undirectional.)
 
 ### Query artists
 
 The graph tree can now be queried by scanning with a prefix of one artist
 (`("The Beatles", …`) and all correlated artists are returned with a weight. The
-top 16 results are kept and saved.
+top-weighted results are kept and saved.
 
 ## Results