Topological Fingerprints for Audio Identification
Wojciech Reise, Ximena Fernández, Maria Dominguez, Heather A. Harrington, Mariano Beguerisse-Díaz
On Spotify, multiple recommender systems enable personalized user experiences across a wide range of product features. These systems are owned by different teams and serve different goals, but all of these systems need to explore and learn about new content as it appears on the platform. In this work, we describe ongoing efforts at Spotify to develop an efficient solution to this problem, by centralizing content exploration and providing signals to existing, decentralized recommendation systems (a.k.a. exploitation systems). We take a creator-centric perspective, and argue that this approach can dramatically reduce the time it takes for new content to reach its full potential.
Wojciech Reise, Ximena Fernández, Maria Dominguez, Heather A. Harrington, Mariano Beguerisse-Díaz
A. Ghazimatin, E. Garmash, G. Penha, K. Sheets, M. Achenbach, O. Semerci, R. Galvez, M. Tannenberg, S. Mantravadi, D. Narayanan, O. Kalaydzhyan, D. Cole, B. Carterette, A. Clifton, P. N. Bennett, C. Hauff, M. Lalmas-Roelleke
Amar Ashar, Karim Ginena, Maria Cipollone, Renata Barreto, Henriette Cramer