5 citations · 6 across the 8 of their papers we have counts for
9 papers
Learning to Re-Draft: A Variational Stackelberg Game for Discrete Diffusion
Dmitrii Moor, Federico Tomasi, Paul N. Bennett +2
Discrete diffusion models offer the ability to re-draft, revisiting and correcting earlier tokens throughout generation. This capability depends on the forward corruption process t…
Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems
Dmitrii Moor, Ben Carterette, Senthilkumar Krishnamoorthy +5
Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendation often involves constructing slates --…
Primal-Dual Guided Decoding for Constrained Discrete Diffusion
Federico Tomasi, Dmitrii Moor, Alice Wang +1
Discrete diffusion models generate structured sequences by progressively unmasking tokens, but enforcing global property constraints during generation remains an open challenge. We…
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…
From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections
Gustavo Penha, Aleksandr V. Petrov, Claudia Hauff +9
The Cranfield paradigm has long provided reliable, reproducible evaluation in ad hoc retrieval, and recent work has begun extending this framework to recommender systems. A recent…
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…
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…
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…