  Hosting about 50 million songs and 4 billion playlists, there is an enormous
amount of data generated at Spotify every single day - upwards of 600 gigabytes
of data (harvard.edu). Since the algorithms that Spotify uses in recommendation
systems is proprietary and confidential, code for big data analytics and
recommendation can only be speculated. However, it is widely theorized that
Spotify uses two main strategies to target users' playlists and personalized
mixes that are infamous for their retention - exploration and exploitation
(kaggle.com). This paper aims to appropriate filtering using the approach of
deep learning for maximum user likeability. The architecture achieves 98.57%
and 80% training and validation accuracy respectively.
