IA316-2020

Spotify Music Recommendation System

Focus on Discover Weekly

I. Dataset

The Parameters of our dataset:

• 943 users

• 1682 songs

• 100000 rows

• 5 keywords per song: title, duration, loudness, tempo, key

• 9 types of event, event strength and listening time per user per song

We generated this dataset on our own:

1. The 5 keywords per song comes from the songs dataset downloaded here https://github.com/thomasSve/Million-Song-Dataset-Analysis/tree/master/datasets

2. The "943 users+ 1682 items+ 100000 rows" structure comes from the 'u.data' used in the notebook "3a_movieLens_dataset_solutions" downloaded here https://github.com/thibaultallart/IA316-2020/tree/master/notebooks

3. The 9 types of event, event strength and listening time per user per song are generated by ourself.

Listening operation	Strength	Subsequent operation	Strength
Have listened  it completely	+1	Like	+1
Listen it frequently	+3	View Song Information	+1
Skip it	-1	Add it to your music	+2
Skip it frequently	-3	Download	+3
NaN	NaN	Don’t recommend it anymore	-2

You can find here

• this dataset named "song_data.csv"

• and a smaller dataset named "smaller_songs_dataset.csv"

II. Notebooks to have a look at

• Coding the main enivironment and the "Random", "UCB", "Linear UCB" agents:

1. "Spotify Recommendation (Large dataset).ipynb" to see the coding of the enivironment and the agents and the experiments using the large dataset of 100000 rows

2. "Spotify_Recommendation (Smaller dataset).ipynb" to see the experiments using a smaller dataset extracted by the condition "user_id<=200".

• Coding the same main enivironment and the "ALS" agent:

3. "ALS_vs_random.ipynb" to see the coding of the "ALS" agent and its regrets.

III. Presentation Slide

You can have a look at our presentation slide here called "Spotify.pdf".