activity
20242026
collaborators
Showing cs.IRShow all

6 papers · 1 filter

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

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…

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.IR2025

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…

cs.IR2024

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…