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

TL;DR
This paper explores the phenomenon of spiking formations in music recommendation embeddings derived from truncated SVD, providing a metric, theoretical insights, and practical implications for evolving music embeddings over time.
Contribution
It introduces a metric to quantify spiking formations, proves their origin related to item communities, and discusses implications for dynamic music recommendation systems.
Findings
Spiking formations are linked to item community structures.
A metric to measure spiking strength is proposed.
Implications for evolving music embeddings are analyzed.
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.
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Taxonomy
TopicsMusic and Audio Processing · Opinion Dynamics and Social Influence · Complex Network Analysis Techniques
