Spotify, the biggest on-demand music service over in the world, embraced records of pushing boundaries in the technological province and steadily using latest technologies to spur success.
Music is changing so quickly, and the landscape of the music industry itself is changing so quickly, that everything new, like Spotify, all feels to me a bit like a grand experiment. -Taylor Swift
With millions of users, the digital music company is progressive via enhancing its assistance and service and also technological wherewithal through numerous acquisitions. Lets's understand how?
Daniel Ek was CEO of uTorrent when Spotify was founded officially in 2006, he initially developed an idea for a service which is better than piracy but also makes restitution for the music industry.
“The seeds of the streaming revolution were planted in late 2006 when Ek put his vision for digital and economic novelty in the music industry. “
In 2008, the Spotify application was introduced in Sweden with the founders Ek and Martin Lorentzon who revealed a license and ownership and deals with large-scale brands like Sony Music Entertainment, Universal Music Group, and Warner Music Group. Later on, the company had amplified in the UK and the US up to 2011.
By the time, Spotify made a proclamation of 40 million paid subscribers and 100 million total users that adhesive streaming in the context of the new fashion of world music utilization in the music industry.
There are half a billion people that listen to music online and the vast majority are doing so illegally. But if we bring those people over to the legal side and Spotify, what is going to happen is we are going to double the music industry and that will lead to more artists creating great new music. - Daniel Ek
Just before that achievement, Fan Insights drove its path for the platform’s back-end, favoring artists and their teams to observe some amount of information about their streaming data, from demographics to geography. In 2017, Fan Insights has provided all artists and their teams a window into their stream, listener, demographic, and geographic patterns on the platform.
With huge investments in Spotify Daniel Ek has committed most of his awaking bits from the last few years to evangelizing the grandeur of streaming music. When more and more global businesses are enduring to come online, Spotify has become a crucial venue for the artists and their teams.
(Suggested blog: Deep Learning Algorithms)
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.
“One of our flagship features is called Discover Weekly. Every Monday, we give you a list of 50 tracks that you haven’t heard before that we think you’re going to like. The ML engine that’s the main basis of it, and it’s advanced some since, had actually been around at Spotify a bit before Discover Weekly was there, just powering our Discover page” – David Murgatroyd, Machine Learning Leader at Spotify.
Spotify deploys a blend of various data aggregation and sorting processes in order to design their specific and powerful recommendation system, powered by machine learning.
Additionally, through this video you can get the perception how Spotify, in actual, uses Big Data, and Artificial Intelligence to deliver an engaged music experience.
Spotify uses a combination of three models to generate “Discover Weekly” model that are;
A famous 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.
Discover weekly data flow, Image credit
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.
Now Spotify has the related keywords that are put up under the shed of “cultural vectors” and “top terms”, and then ths songs and their artists associated with these terms.
Specific weight is also calculated for the terms that are imperative than others (it shows the number of times individuals will be associated with their artists or songs). It also allows Spotify to recognize trending music terms.
(Also check: Top NLP Libraries with Python)
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.
However, through leveraging songs data from audio models, collaborative filtering models are able to decipher the track and recommend it like users with other popular songs.
Also, Spotify has embraced convolutional neural networks (CNN) on audio data, 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 instance, songs that have high rhythm, acoustic or energetic are put together under one category, then CNN operates on that with huge efficiency and certainty to categorize songs in corresponding groups.
With more and more users continuing to avail the Spotify Platform, its vying assets, stemming from user data and analytics and other trending technologies, will steadily expand.
Spotify will be capable of using data for making more personalized experience and engaging users into the service.
As the amount of data evolves, Spotify will obtain more leverage across recording studios and artists who are requiring access to data for making business decisions.
Spotify can invest in the podcast space on a large scale and leverage their information on the preferences of users to promote latest content and grow engagement, also to remodel their existing recommendation engine to design similar features for podcasts.
Spotify improves listening experience of users
Spotify should be careful not to disaffect fans or artists implementing the services when they are looking to invest in the data spaces.
Spotify is able to compose music with AI tools that removes artists from the process completely. So, companies should carefully express the aim of technology so that creative talent stays tuned with the platform.
In reference to the user perspective, Spotify should be observant of user privacy and data protection laws as they entirely depend on user’s data for all surfaces of their business.
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.
I think the next big thing in music, and it's kind of because I come from the tech industry, is actually, I think it's the platform... Spotify is incredibly interesting. I think the platform is becoming the star. -Brian Chesky
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.
6 Major Branches of Artificial Intelligence (AI)READ MORE
Reliance Jio and JioMart: Marketing Strategy, SWOT Analysis, and Working EcosystemREAD MORE
Top 10 Big Data TechnologiesREAD MORE
8 Most Popular Business Analysis Techniques used by Business AnalystREAD MORE
Deep Learning - Overview, Practical Examples, Popular AlgorithmsREAD MORE
7 Types of Activation Functions in Neural NetworkREAD MORE
What Are Recommendation Systems in Machine Learning?READ MORE
7 types of regression techniques you should know in Machine LearningREAD MORE
Introduction to Time Series Analysis in Machine learningREAD MORE
How Does Linear And Logistic Regression Work In Machine Learning?READ MORE