collaborators

5 papers

cs.IR2026

Deploying Semantic ID-based Generative Retrieval for Large-Scale Podcast Discovery at Spotify

Edoardo D'Amico, Marco De Nadai, Praveen Chandar +41

Podcast listening is often grounded in a set of favorite shows, while listener intent can evolve over time. This combination of stable preferences and changing intent motivates rec…

cs.IR2026

A Unified Language Model for Large Scale Search, Recommendation, and Reasoning

Marco De Nadai, Edoardo D'Amico, Max Lefarov +18

LLMs are increasingly applied to recommendation, retrieval, and reasoning, yet deploying a single end-to-end model that can jointly support these behaviors over large, heterogeneou…

cs.IR2025

Semantic IDs for Joint Generative Search and Recommendation

Gustavo Penha, Edoardo D'Amico, Marco De Nadai +8

Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models…

cs.IR2025

Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge

Francesco Fabbri, Gustavo Penha, Edoardo D'Amico +7

Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bia…

cs.CL2025

Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models

Bernd Huber, Ghazal Fazelnia, Andreas Damianou +6

Large language models (LLMs) excel at generating contextually relevant content. However, tailoring these outputs to individual users for effective personalization is a significant…