Of Spiky SVDs and Music Recommendation
Darius Afchar Romain Hennequin Vincent Guigue
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.