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

Abstract

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.

Related

November 2021 | ISMIR - International Society for Music Information Retrieval Conference

Multi-Task Learning of Graph-based Inductive Representations of Music Content

Antonia Saravanou, Federico Tomasi, Rishabh Mehrotra and Mounia Lalmas

November 2021 | CIKM

Leveraging Semantic Information to Facilitate the Discovery of Underserved Podcasts

Maryam Aziz, Alice Wang, Aasish Pappu, Hugues Bouchard,Yu Zhao, Benjamin Carterette and Mounia Lalmas

October 2021 | CSCW

Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?

Ziang Xiao, Sarah Mennicken, Bernd Huber, Adam Shonkoff, Jennifer Thom