Role of AI/ML in winning the Music Streaming World : Spotify

Spotify is by far, the largest on-demand music streaming application today. The firm has a record of pushing boundaries in technology by using AI and machine learning to enhance the user experience through customer data insights.

With tens of millions of users listening to music every minute of the day, brands like Spotify accumulate a mountain of implicit customer data comprised of song preferences, keyword preferences, playlist data, geographic location of listeners, most used devices and more .

Data plays crucial role in every decision in Spotify . This data is used to train multiple algorithm to take decisions depending on their previous experiences. Spotify’s application hosts over 50M songs and 4B playlists, garnering massive amounts of data related to song preferences, search behavior, playlist data, geographic location and most used devices. Spotify performs analysis and creates machine learning algorithms based on this data to understand music tastes and ease discovery of new genres, artists and songs.

Spotify deploys a blend of various data aggregation and sorting processes in order to design their specific and powerful recommendation model that is powered by machine learning.

Spotify offers a great personalized weekly playlist called “Discover Weekly”, one of its flagship features. Every Monday, each user receives a latest playlist of new recommended songs, made to their personalized choice based on their listening history and the songs they are interested in.Machine learning enables the recommendations to improve over time. Not only does it keep users returning, it also enables greater exposure for artists who users may not search for organically.

For generating such Weekly feeds Spotify team uses cobination of below three Models:-

1.Collaborative Filtering:-

It is afamous technique deployed by recommender systems, Collaborative Filtering, to make predictions about the user’s preferences on the basis of similar user preferences.

In Spotify, the Collaborative Filtering algorithms examine several user-created playlists having songs the users used to listen to. The algorithm adjusts playlists after looking at other songs that come up in the playlists and recommends those songs.

2. Natural Language Processing(NLP):-

NLP helps in analyzing human speech via text.

NLP is through which machines learn human language, in context of this Spotify uses it as AI-powered Spotify browsing, i.e. tracks metadata, blog posts, latest artists and songs on the internet, discussion about musicians, news articles, etc.

This helps Spotify to understand what explicitly everyone is discussing about music, about songs and artists. From all this, it selects descriptive terms, phrases and other associated texts.

3.Audio Model

Audio models are implemented to evaluate data from the raw audio tracks and classify songs appropriately, it aids the app analyzes all songs to construct recommendations. For example, if a new song is released by a new artist on the platform, the NLP model might not choose if social media is low or if it converges online.

Spotify has also adopted convolutional neural networks, which happen to be the same technology used for facial recognition. In the case of Spotify these models are used on audio data instead of on pixels.For more information go through this blog

Conclusion

Spotify is best known for its user experience, music recommendation that is constantly getting improved. In terms of technology it uses Artificial Intelligence, Big Data and Machine learning in order to upgrade and customize the music experience for listeners. Undoubtedly, Spotify demands no introduction, it is one of the excellent music streaming apps in the market.

Possessing millions of users and billion hours of monthly listening, Spotify augments artists stretch a multitude of music fans across the world. You have learned the past experience, features, opportunities and challenges for Sopitfy and how it uses recommendation engines to provide enhanced listening experience.

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