47 citations · 48 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Model Selection for Production System via Automated Online Experiments
Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia +2
A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production. As a model is often an intermediate component…
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
James McInerney, Brian Brost, Praveen Chandar +2
Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner. Providing and evaluating good sequences of…
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation
Carlos Fernández-Loría, Foster Provost, Jesse Anderton +2
This study presents a systematic comparison of methods for individual treatment assignment, a general problem that arises in many applications and has received significant attentio…
The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
David Holtz, Benjamin Carterette, Praveen Chandar +3
It remains unknown whether personalized recommendations increase or decrease the diversity of content people consume. We present results from a randomized field experiment on Spoti…