Live Session
Friday Posters
Main Track
Of Spiky SVDs and Music Recommendation
Darius Afchar (Deezer Research), Romain Hennequin (Deezer Research) and Vincent Guigue (AgroParisTech).
Abstract
The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval or embedding musical items for downstream tasks. This paper investigates a curious effect that we show naturally occurring on many recommendation datasets: spiking formations in the embedding space. We first propose a metric to quantify this spiking organization’s strength, then mathematically prove its origin tied to underlying communities of items of varying internal popularity. With this new-found theoretical understanding, we finally open the topic with an industrial use case of estimating how music embeddings’ top-k similar items will change over time under the addition of data.