Tag: algorithm

Responsibly Balancing What Goes Into Your Personalized Recommendations

March 6, 2023

Every month, tens of billions of discoveries happen on Spotify. Personalized recommendations play an important role in our ability to match listeners around the world with the right content, tracks, artists, or creators at the right moment. Behind the scenes, we combine human editorial expertise with a multitude of signals and systems with the aim of providing every listener with a unique and safe experience. 

At Spotify we focus on delivering recommendations that are relevant, encourage diversity in listening, and provide the opportunities for artist and creator discovery. We spoke with Henriette Cramer, Director of Algorithmic Impact, and Amar Ashar, Head of Algorithmic Policy—both members of the Trust & Safety team—for a deeper look at algorithmic impact and safety. 

Why focus on algorithmic impact? 

Henriette: Algorithmically programmed experiences like Discover Weekly, Release Radar, and Made for You Mixes, or even Search, provide opportunities for artists and podcast creators to grow their fan bases. But while machine learning and algorithms enable these really important opportunities, we know we have a responsibility to mitigate unintended harms, ensure we represent a very wide range of global creators on our platform, and understand our impact.

Understanding our algorithmic impact requires extensive internal and external collaboration, and we approach this space through three channels: research, product engagement, and collaboration with external partners. It’s an ever-evolving field, and we’re proactively working with Spotify teams and external stakeholders to continuously improve our approach as we continue to learn. 

What makes Spotify unique, from an algorithmic perspective? 

Amar: People often talk about the “Spotify Algorithm,” but that’s an oversimplification. In fact, Spotify’s personalization is a combination of a variety of algorithms, along with editorial and data curation teams, all contributing to a unique experience for each listener.

Spotify editors play a crucial role within this space by using their expert judgment to curate playlists and help artists find new fans. They also work with algorithms to create highly situational and personalized experiences. We call this “algotorial”—bringing both the editorial and algorithmic worlds together. This collaboration is critical to the Spotify experience. Think of it this way: Algorithms don’t go out to concerts, people do, which is why human expertise is an essential ingredient in our recommendations. 

We just released a new AI DJ that delivers a curated lineup of music alongside commentary around the tracks and artists. How are teams at Spotify working together to make sure the safety of recommendations is prioritized?

Henriette: In general, ensuring we approach Spotify recommendations responsibly requires close coordination between lots of teams across product, policy, legal, and research. We work with each of them to provide guidance that’s reflective of our algorithmic equity and safety goals, and we use various tools, such as algorithmic assessments, that help us identify and solve problems before they happen. 

Spotify’s DJ takes a unique approach by combining Spotify’s personalization technology, generative AI in the hands of music editors, and voice technology. The expertise of our editors is something that’s really important to our philosophy. As we launch new features, we aim for appropriate safety measures and processes to be in place. The product has been tested in a closed environment for a while, and now that we have launched this product as a beta, we’ll continue to study and improve the experience. 

How does your team work with external partners to improve Spotify’s personalized experience?

Amar: Engaging with research communities outside of Spotify is imperative to do our work. That’s why we also continue to share our findings with the wider community, collaborate across sectors, and ensure, as an industry, that we keep learning and evolving existing practices. 

We also work closely with external partners through Spotify’s Safety Advisory Council, which includes an interdisciplinary group of experts who advise us on safety topics and bring expertise on recommendations, responsibility, and safety from a global perspective.

What’s your go-to playlist?  

Amar: Discover Weekly, not only because it’s consistently a great playlist that has introduced me to new artists and genres, but also because I’ve been lucky enough to have worked with the team that’s built this flagship product.   

Henriette: So many! I love editorial playlists like Techno Bunker, Queens of the Blues, or New Orleans Brass to really get into a genre. Since I worked on voice projects in the past, it’s been really nice to play with the new DJ beta and see editorial, tech, and design work shine together as we continue to study how we can use new techniques responsibly.

Amplifying Artist Input in Your Personalized Recommendations

November 2, 2020

Listeners enjoy Spotify because we introduce them to music to fall in love with—including music they might not have found otherwise. In fact, Spotify drives 16 billion artist discoveries every month, meaning 16 billion times a month, fans listen to an artist they have never heard before on Spotify. We’re proud of that and are actively refining our algorithms to enable even more fan discoveries of new artists each month.

We’re able to make great personalized recommendations because of complex, dynamic systems that consider a wide variety of inputs about what you like—which we refer to as signals—and balance those signals in many possible different pathways to produce an output: the perfect song for the moment, just for you. 

This might sound complicated—and it is! Our personalized recommendations take into account thousands of types of signals: what you’re listening to and when, which songs you’re adding to your playlists, the listening habits of people who have similar tastes, and much more. In order to create algorithms that truly deliver the right song for the right time, we’re also taking into account less obvious factors: things like time of day, or the order in which you’re listening to songs or podcasts, or the release date of a song. 

Artists tell us they want more opportunities to connect with new listeners, and we believe our recommendations should also be informed by artists—their priorities and what they have to say about their music. And soon, we will roll out a test of a service that gives artists a say in how their music is discovered. 

In this new experiment, artists and labels can identify music that’s a priority for them, and our system will add that signal to the algorithm that determines personalized listening sessions. This allows our algorithms to account for what’s important to the artist—perhaps a song they’re particularly excited about, an album anniversary they’re celebrating, a viral cultural moment they’re experiencing, or other factors they care about. 

To ensure the tool is accessible to artists at any stage of their careers, it won’t require any upfront budget. Instead, labels or rights holders agree to be paid a promotional recording royalty rate for streams in personalized listening sessions where we provided this service. If the songs resonate with listeners, we’ll keep trying them in similar sessions. If the songs don’t perform well, they’ll quickly be pulled back. Listener satisfaction is our priority—we won’t guarantee placement to labels or artists, and we only ever recommend music we think listeners will want to hear. 

We’re testing this to make sure it’s a great experience for both listeners and artists. To start, we’ll focus on applying this service to our Radio and Autoplay formats, where we know listeners are looking to discover new music. As we learn from this experiment, we’ll carefully test expanding to other personalized areas of Spotify. 

We believe this new service will unlock more discoveries than ever before. Our recommendations rely on signals from you, so keep on listening to what you love!