arXiv:2606. 13858v1 Announce Type: cross Abstract: Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2606. 00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content.
arXiv:2609.23877v1 Announce Type: new Abstract: Modern music streaming platforms face a persistent tradeoff: exploiting familiar content versus driving the exploration of novel items. While users fre...
arXiv:2605. 03395v2 Announce Type: replace-cross Abstract: Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems.
The paper introduces SARA, an industrial framework that scales articulated user rationales (AURs) for recommendation systems. It curates a high‑quality AUR dataset from 240 M users, trains a 7B‑parameter MLLM (SARA‑7B) to generate rationales for millions of authors, and integrates these generated rationales into a production ranking model (SARA‑Ranker). Offline and online experiments demonstrate that the system produces more specific, polarity‑consistent rationales and improves user engagement while reducing negative feedback.
arXiv:2607. 06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments.
arXiv:2607. 03296v1 Announce Type: cross Abstract: Crossmodal correspondences between sound and taste are well established in psychology and neuroscience, but largely absent from content-based multimedia retrieval.