5 citations · 9 across the 11 of their papers we have counts for
12 papers
Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale
Ghazal Fazelnia, Paul Gigioli, Eliza Klyce +16
Foundation model recommender systems require user context that can be consumed by large language models, reasoned over, and refined through natural-language interaction. Traditiona…
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…
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…
Mapping Faithful Reasoning in Language Models
Jiazheng Li, Andreas Damianou, J Rosser +2
Chain-of-thought (CoT) traces promise transparency for reasoning language models, but prior work shows they are not always faithful reflections of internal computation. This raises…
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Marco De Nadai, Andreas Damianou, Mounia Lalmas
Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features d…
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…
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…
Text2Tracks: Prompt-based Music Recommendation via Generative Retrieval
Enrico Palumbo, Gustavo Penha, Andreas Damianou +5
In recent years, Large Language Models (LLMs) have enabled users to provide highly specific music recommendation requests using natural language prompts (e.g. "Can you recommend so…
Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Models
Konstantina Palla, José Luis Redondo García, Claudia Hauff +5
Content moderation plays a critical role in shaping safe and inclusive online environments, balancing platform standards, user expectations, and regulatory frameworks. Traditionall…
Generating Query Recommendations via LLMs
Andrea Bacciu, Enrico Palumbo, Andreas Damianou +2
Query recommendation systems are ubiquitous in modern search engines, assisting users in producing effective queries to meet their information needs. However, these systems require…
Towards Graph Foundation Models for Personalization
Andreas Damianou, Francesco Fabbri, Paul Gigioli +4
In the realm of personalization, integrating diverse information sources such as consumption signals and content-based representations is becoming increasingly critical to build st…
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks
Marco De Nadai, Francesco Fabbri, Paul Gigioli +11
In the ever-evolving digital audio landscape, Spotify, well-known for its music and talk content, has recently introduced audiobooks to its vast user base. While promising, this mo…