Our paper on refusal in LLMs is finally up on arXiv.

Delighted to share the acceptance of our paper "PODTILE: Facilitating Podcast Episode Browsing With Auto-generated Chapters" at #CIKM2024. This work was a great cross-disciplinary collaboration at Spotify.

Excited to kick-off #KDD2024 (kdd2024.kdd.org) on Sunday and super proud to be General Chair (together with Ricardo Baeza-Yates) of such an event. More than 2200 attendees from all over the globe, 82 organizers, 133 student volunteers, and an incredible list of generous sponsors

Super excited to welcome SIGKDD 2025 in Barcelona 🚀 I’ll be at the conference this week, so feel free to ping me to talk about GNNs, GenAI for recs or anything Barcelona related 🎉

Video data is widely available but has been difficult to analyze automatically Particularly, TV data is a rich and important source of information for millions. We have a paper at #KDD2024 making such data accessible. Paper: gvrkiran.github.io/content/TV_Deb…

🚨Hiring alert! My team Spotify Research is looking to hire a research scientist with a machine learning background! Come help us improve personalization experiences by integrating innovative generative AI technologies into our recommender systems. lifeatspotify.com/jobs/research-…

Uncovering Coordinated Cross-Platform Information Operations Threatening the Integrity of the 2024 U.S. Presidential Election Online Discussion w/ Emilio Ferrara Marco Minici Federico Cinus View our findings here: papers.ssrn.com/sol3/papers.cf…

🎉Excited to share: "Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences". Joint work with Erica Coppolillo, Simone Mungari, Ettore Ritacco, Francesco Fabbri, Marco Minici, Francesco Bonchi arxiv.org/abs/2409.16478

I am recruiting 1-2 PhD students for Fall 2025 at Princeton to work on Comp Social Science/Societal Impact of GenAI/GenAI for SocSci I wrote a bit about my research flavor & interests here: manoelhortaribeiro.github.io/advising Deadline: December 15th (cs.princeton.edu/grad#prospecti…) Please boost!

It was a true honour to present our research at Spotify during #RecSys2024. Sharing our work in such a unique theatre setting was an unforgettable experience. Thank you to everyone involved!

📢 I’m recruiting PhD students for Fall'25 in UC Berkeley EECS or UC Joint Computational Precision Health Program! Please apply if you have related interests in AI for social good / networks / public health / social science See my website for more on my research and how to apply: serinachang5.github.io Please RT!

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What a Christmas gift🎁In our latest AAAI paper, we integrate our coordination detection model with the power of LLMs & GNNs to enhance the generalizability of information operation (IO) detection in data-scarce scenarios and unseen IOs. Kudos to Marco Minici and our🌠team!

Internship opportunity at Spotify! PhD students, come join our Tech Research Team in London. When: June-August 2025 Last day to apply is *February 6th, 2025* at 5 PM GMT. Apply here lifeatspotify.com/jobs/2025-summ…

Can we effectively detect covert Information Operations (IOs) that attempt to manipulate socio-political debates on social media? This is the focus of our work, "IOHunter: Graph Foundation Model to Uncover Online Information Operations", just presented at the AAAI

Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge Spotify introduces a profile-aware LLM framework for evaluating personalized podcast recommendations using natural-language user profiles distilled from listening history. 📝arxiv.org/abs/2508.08777

What if we could use off-the-shelf Multimodal Large Language Model to enrich current video recommendation models? This is what we asked ourselves in our recent #recsys2025 paper arxiv.org/pdf/2508.09789 🧵

Semantic IDs for Joint Generative Search and Recommendation Gustavo Penha et al. at Spotify introduce a bi-encoder model fine-tuned on both search and recommendation tasks to obtain item embeddings, followed by construction of unified Semantic ID space. 📝arxiv.org/abs/2508.10478

Excited to share our paper “Semantic IDs for Joint Generative Search & Recommendation” @ RecSys'25 🧠 Jointly fine-tuning embeddings for both tasks → shared Semantic IDs that work for search and recs ⚖️ 📦 No more task-specific trade-offs!

Happy to share our #recsys25 paper: “Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge”. 🧠 90 days of listening → natural-language user profiles → LLM judges alignment 📊 Aligns with human eval. With amazing Spotify co-authors. 📄 arxiv.org/abs/2508.08777