Audio-Based Musical Version Identification: Elements and challenges


This tutorial article provides a review of key ideas and approaches proposed in 20 years of VI research, and to connect them to current practice. For more than a decade, VI systems suffered from the accuracy-scalability trade-off, with attempts to increase accuracy resulting in cumbersome, non-scalable systems. Recent years however have witnessed an increase in VI approaches based on deep learning systems that take a step toward bridging the accuracy-scalability gap, and we start seeing the possibility to deploy such systems in real-world applications. Although this trend positively influences the number of researchers and institutions working on VI, it may also result in obscuring the literature before the deep learning era. To appreciate years of novel ideas in VI and to facilitate building better systems, we believe that now may be the right time to review some of the successful ideas and applications proposed in VI literature and to connect them to current systems.


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

April 2024 | ICLR

In-context Exploration-Exploitation for Reinforcement Learning

Zhenwen Dai, Federico Tomasi, Sina Ghiassian