22 citations · 26 across the 5 of their papers we have counts for
6 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…
Developing a Recommendation Benchmark for MLPerf Training and Inference
Carole-Jean Wu, Robin Burke, Ed H. Chi +4
Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enh…
Gradient Boosted Decision Tree Neural Network
Mohammad Saberian, Pablo Delgado, Yves Raimond
In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees. We first illustrate how to convert a learned ensemble of decision tree…
Beta Survival Models
David Hubbard, Benoit Rostykus, Yves Raimond +1
This article analyzes the problem of estimating the time until an event occurs, also known as survival modeling. We observe through substantial experiments on large real-world data…