10 citations · 14 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
The Disconnect Between Better Descriptive Reasoning Trace Quality and Recommendation Effectiveness
Gustavo Penha, Juan Elenter, Claudia Hauff +3
Recent work has focused on improving explicit natural-language descriptive reasoning traces for generative recommendation. This includes systems that augment semantic ID (SID) pred…
Do Sequential Recommendation Benchmarks Really Require Higher-Order Sequence Modelling?
Aleksandr V. Petrov, Praveen Chandar, Paul N. Bennett +2
Sequential recommenders increasingly use language-model architectures designed to capture complex, context-dependent interactions. Yet it remains unclear whether widely used benchm…
Efficient Dataset Selection for Continual Adaptation of Generative Recommenders
Cathy Jiao, Juan Elenter, Praveen Ravichandran +7
Recommendation systems must continuously adapt to evolving user behavior, yet the volume of data generated in large-scale streaming environments makes frequent full retraining impr…
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…