Data Skeptic29 Okt 2025

Music Playlist Recommendations

In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation systems. She explores three key types of fairness—group, individual, and counterfactual—and examines how algorithms create challenges like popularity bias (favoring mainstream content) and multi-interest bias (underserving users with diverse tastes). Rebecca introduces LARP, her multi-stage multimodal framework for playlist continuation that uses contrastive learning to align text and audio representations, learn song relationships, and create playlist-level embeddings to address the cold start problem.

A significant contribution of Rebecca's work is the Music Semantics dataset, created by scraping Reddit discussions to capture how people naturally describe music using atmospheric qualities, contextual comparisons, and situational associations rather than just technical features. This dataset, available on Hugging Face, enables more nuanced recommendation systems that better understand user preferences and support niche tastes. Her research utilizes industry datasets including Last.fm and Spotify's Million Playlist Dataset, and points toward exciting future applications in music generation and multimodal systems that combine audio, text, and video.

Denne episoden er hentet fra en åpen RSS-feed og er ikke publisert av Podme. Den kan derfor inneholde annonser.

Episoder(601)

GraphText

On the show today, we are joined by Jianan Zhao, a Computer Science student at Mila and the University of Montreal. His research focus is on graph databases and natural language processing. He joins u...

31 Okt 202330min

arXiv Publication Patterns

Today, we are joined by Rajiv Movva, a PhD student in Computer Science at Cornell Tech University. His research interest lies in the intersection of responsible AI and computational social science. He...

23 Okt 202328min

Do LLMs Make Ethical Choices

We are excited to be joined by Josh Albrecht, the CTO of Imbue. Imbue is a research company whose mission is to create AI agents that are more robust, safer, and easier to use. He joins us to share fi...

16 Okt 202329min

Emergent Deception in LLMs

On today's show, we are joined by Thilo Hagendorff, a Research Group Leader of Ethics of Generative AI at the University of Stuttgart. He joins us to discuss his research, Deception Abilities Emerged ...

9 Okt 202327min

Agents with Theory of Mind Play Hanabi

Nieves Montes, a Ph.D. student at the Artificial Intelligence Research Institute in Barcelona, Spain, joins us. Her PhD research revolves around value-based reasoning in relation to norms. She shares ...

2 Okt 202338min

LLMs for Evil

We are joined by Maximilian Mozes, a PhD student at the University College, London. His PhD research focuses on Natural Language Processing (NLP), particularly the intersection of adversarial machine ...

25 Sep 202326min

The Defeat of the Winograd Schema Challenge

Our guest today is Vid Kocijan, a Machine Learning Engineer at Kumo AI. Vid has a Ph.D. in Computer Science at the University of Oxford. His research focused on common sense reasoning, pre-training in...

11 Sep 202331min

LLMs in Social Science

Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is c...

4 Sep 202334min