We have an open research scientist position in our lab at Spotify, Personalization ! The areas of expertise are: Information Retrieval, Recommendation System, Language Technologies, Foundational Models, Generative AI Technologies, and Machine Learning. lifeatspotify.com/jobs/research-…

I am attending #ECIR25 at Lucca 🇮🇹 if you are interested and want to discuss this position!

Contextualizing Spotify's Audiobook List Recommendations with Descriptive Shelves Spotify introduces a pipeline that generates personalized audiobook recommendations with descriptive shelves to help users explore content based on their interests. 📝arxiv.org/abs/2504.13572

Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment Adam Yang et al. leverage LLM alignment techniques to fine-tune models for generating query expansions that directly optimize retrieval effectiveness. 📝arxiv.org/abs/2507.11042

Adaptive Repetition for Mitigating Position Bias in LLM-Based Ranking Spotify introduces a dynamic early-stopping method that adaptively determines repetitions needed for each ranking instance, reducing LLM calls by 81% while preserving accuracy. 📝arxiv.org/abs/2507.17788

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

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations Marco De Nadai et al. at Spotify use multimodal LLMs to generate natural-language descriptions of video content for better recommendations 📝arxiv.org/abs/2508.09789 👨🏽‍💻huggingface.co/datasets/marco…

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

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

I doubt to what extent improvements on these datasets would translate to improvements in today's real-world recommendation settings. Reference: arxiv.org/abs/2508.19399…

For anyone worried their LLM might be making stuff up, we made a budget‐friendly truth serum (semantic entropy + Bayesian). See for yourself: youtube.com/watch?v=x_8ORG… Paper: arxiv.org/pdf/2504.03579