Update dataset to remove ambiguous rows

README.md

@@ -33,10 +33,12 @@ Training a model to generate high quality recommendations is hard because most u
 
 This dataset contains the user, artist, play count records. The user may have provided additional details such as their gender, age and location. These additional details are both optional and unverified.
 
-Where possible the musicbrainz ids for the artist has been provided for disambiguation.
+Last.fm provides a service known as scrobbling where you can submit tracks you have listened to in other apps. When you submit these you do not have to provide musicbrainz ids and as such these can be ambiguous. To make this dataset easier to use I have removed rows where the musicbrainz identifiers are missing.
 
 The train, validation and test splits have separate users with no overlap.
 
+To make training on this dataset easier I have added the user_index and artist_index columns. These are integer columns that uniquely identify every user and artist.
+
 ### Dataset Source
 
 The original data was collected by [Òscar Celma @ MTG/UPF](http://ocelma.net/MusicRecommendationDataset/lastfm-360K.html).

test.gz.parquet

@@ -1,3 +1,3 @@
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+oid sha256:197ef7aae83c72e020458f7679885069aa93f76ed80491bdb58a401cfc844dd7
+size 39975459

train.gz.parquet

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+size 328916501

valid.gz.parquet

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-size 39309451
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