We wrote a post summarizing our #RecSys2024 paper on bridging search and recommendation with generative retrieval 🧵 (1/N)
https://t.co/vmG7iLr1Em
w. @AliVardasbi, @denadai2, @enricopalumbo91, Hugues Bouchard
For anyone worried their LLM might be making stuff up, we made a budget‐friendly truth serum (semantic entropy + Bayesian). See for yourself: https://t.co/gq8oFP5Eqr
Paper: https://t.co/s6SuQW4sAM
I doubt to what extent improvements on these datasets would translate to improvements in today's real-world recommendation settings. Reference: https://t.co/YPcJdfMy2p
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.
📄 https://t.co/JpdOLQ3yRs
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!
Semantic IDs for Joint Generative Search and Recommendation
@_Guz_ 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.
📝https://t.co/mTERH16wib
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.
📝https://t.co/Rk8qUS0V2P
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
@denadai2 et al. at Spotify use multimodal LLMs to generate natural-language descriptions of video content for better recommendations
📝https://t.co/zloHLosVzd
👨🏽💻https://t.co/PlWzE5TRWp
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 https://t.co/1dqzYgM8LR
🧵
🔎 LLM alignment techniques can enhance query expansion by eliminating the need for multiple generations followed by re-ranking/filtering steps.
Check out this work led by @adam_x_yang during his internship with us at @SpotifyResearch w. @enricopalumbo91 and Hugues Bouchard⬇️
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.
📝https://t.co/TjcyF0RvlG
Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment
@adam_x_yang et al. leverage LLM alignment techniques to fine-tune models for generating query expansions that directly optimize retrieval effectiveness.
📝https://t.co/sAnWpP67wq
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.
📝https://t.co/dzJvBkOzZI
We just published this blog post about our research on music track search with generative retrieval. 🧵
With @enricopalumbo91@adamianou@peputo Timothy Christopher, Alice Wang, Hugues Bouchard, @mounialalmas