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Collaborative filtering, aiming to describe the track in its connection with other tracks on the platform by studying user-generated assets.Content-based filtering, aiming to describe the track by examining the content itself.Spotify's approach to track representation is made up of two primary components: Let's break down exactly how this process works - starting with the track/artist representations: Generating Track Representations: Content-based and Collaborative filtering On each side of that proposition, Spotify employs several independent ML models and algorithms to generate item representations and user representations. For this recommendation system to work, it needs to understand the content it recommends and the users it recommends it to. In broad strokes, at the core of any AI recommender system, there's an ML model optimized for the key business goals: user retention, time spent on the platform, and, ultimately, generated revenue. Behind the algorithm: understanding music and user tastes Don't worry, though: we'll make it clear once we depart the land of facts.

That is not to say that we don't know anything definitive on how the system works - in fact, a healthy chunk of Spotify's recommendation approach has been widely publicized - but we would have to descend into the area of educated guesses when it comes to some of the more granular details. What we do have is the company's extensive public R&D records, its API, and some common sense. However, as opposed to TikTok, in this case, we don't have the courtesy of recently leaked internal documentation to uncover the makeup of the system. In a lot of ways, Spotify's recommendation engine is dealing with a similar flow as TikTok's 'For You' algorithm, playing the matchmaker between the creators (or artists) and users (or fans) on a two-sided marketplace. How recommendation and music discovery works on Spotify? This time around, we'll dive deeper into the topic with a breakdown of Spotify's recommender system (which can be, to an extent, extrapolated to other DSP recommendation engines). A few weeks back, we kicked off the year with an article covering the ins and outs of the famed TikTok 'For You' algorithm. The topic of unveiling AI-driven recommender systems and providing music professionals with the resources and tools they need to understand and manage these algorithms will be a big focus for Music Tomorrow throughout 2022.

SPOTIFY MUSIC MOODS MEANING HOW TO

Music professionals rely on recommender systems across platforms like Spotify and YouTube to amplify the ad budgets, connect with the new audiences, and all-around execute successful release campaigns - while often having no clear vision of how these systems operate and how to leverage them to amplify artist discovery.

SPOTIFY MUSIC MOODS MEANING PROFESSIONAL

Yet, as algorithmic recommendations take center stage in the music discovery landscape, the professional community at large still perceives these recommender algorithms as black boxes. On Spotify, for instance, over one third of all new artist discoveries happen through 'Made for You' recommendation sessions according to the recently released Made to be Found report. Back in 2020, as much as 62% of consumers rated across platforms like Spotify and YouTube among their top sources of music discovery - and be sure that a healthy chunk of that discovery is going to be mediated by recommender systems.

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If you're curious to find out more about how artists and their team can influence the recommender system to optimize algorithmic traffic, check out our ongoing series on Recommender System Optimization.Īs we move ahead into the 2020s, an ever-increasing share of music consumption and discovery is going to be mediated by AI-driven recommendation systems. This article gets into great detail on how Spotify Recommender System works, outlining the process Spotify recommender follows to understand assets and users on the platform.

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