Algorithmic Balancing of Familiarity, Similarity, & Discovery in Music Recommendations


Algorithmic recommendations shape music consumption at scale, and understanding the role different behavioral aspects play in how content is consumed, is a central question for music streaming platforms. Focusing on the notions of familiarity, similarity and discovery, we identify the need for explicit consideration and optimization of such objectives, and establish the need to efficiently balance them when generating algorithmic recommendations for users. We posit that while familiarity helps drive short term engagement, jointly optimizing for discovery enables the platform to influence and shape consumption across suppliers. We propose a multi-level ordered-weighted averaging based objective balancer to help maintain a healthy balance with respect to familiarity and discovery objectives, and conduct a series of offline evaluations and online AB tests, to demonstrate that despite the presence of strict trade-offs, we can achieve wins on both satisfaction and discover centric objectives. Our proposed methods and insights have implications for the design and deployment of practical approaches for music recommendations, and our findings demonstrate that they can lead to substantial improvements on recommendation quality on one of the world’s largest music streaming platforms.


May 2024 | Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard

Structural Podcast Content Modeling with Generalizability

Yijun Tian, Maryam Aziz, Alice Wang, Enrico Palumbo and Hugues Bouchard

May 2024 | The Web Conference

Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks

Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou

May 2024 | The Web Conference (GFM workshop)

Towards Graph Foundation Models for Personalization

Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas