Add README with metadata

README.md

@@ -0,0 +1,61 @@
+---
+pretty_name: "Last.fm user artist song dataset"
+tags:
+- music
+size_categories:
+- 10M<n<100M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "data/train.gz.parquet"
+  - split: valid
+    path: "data/valid.gz.parquet"
+  - split: test
+    path: "data/test.gz.parquet"
+---
+
+## Dataset Description
+
+This dataset is ideal for training a recommendation system that incorporates time and country information.
+
+### Task Summary
+
+A recommender system, or a recommendation system, is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.
+
+Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are used in a variety of areas, with commonly recognised examples taking the form of playlist generators for video and music services, product recommenders for online stores, or content recommenders for social media platforms and open web content recommenders. These systems can operate using a single type of input, like music, or multiple inputs within and across platforms like news, books and search queries. There are also popular recommender systems for specific topics like restaurants and online dating. Recommender systems have also been developed to explore research articles and experts, collaborators, and financial services.
+
+[Wikipedia: Recommender System](https://en.wikipedia.org/wiki/Recommender_system)
+
+Training a model to generate high quality recommendations is hard because most users never review the products or services that they use. Implicit recommendations are available for training and are typically based on interactions with the products or services. While this data is abundant it lacks negative examples, as the interaction that is used for training has occurred even if the user did not like the product or service.
+
+### Dataset Summary
+
+This dataset contains the user, timestamp, artist, song 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 and song have been provided for disambiguation.
+
+The train, validation and test splits have separate users with no overlap.
+
+### Dataset Source
+
+The original data was collected by [Òscar Celma @ MTG/UPF](http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html).
+
+### Dataset License
+
+The data contained is distributed with permission of Last.fm. The data is made available for non-commercial use. Those interested in using the data or web services in a commercial context should contact: [email protected]
+
+For more information see Last.fm [terms of service](http://www.last.fm/api/tos).
+
+### Citation Information
+
+When using this dataset you must reference the [Last.fm](http://last.fm/) webpage.
+
+```
+@book{Celma:Springer2010,
+    author = {Celma, O.},
+    title = {{Music Recommendation and Discovery in the Long Tail}},
+    publisher = {Springer},
+    year = {2010}
+}
+```