CIIR Talk Series: Paul Bennett

Speaker: Paul Bennett, Spotify

Title: Toward AI-Powered Next Generation Personalized Experiences

Abstract: As the recent pace of AI innovation has increased, search and recommendation is being increasingly transformed in recent years. These transformations are fundamentally changing how people interact with search and recommendation experiences as well as how content is created. Understanding these trends is a key toward projecting the broader technology needs we anticipate in the future. After reflecting on these broader trends we dive into three key advances that have already changed the landscape of search and recommendation: dense retrieval, augmented LLMs, and GNNs. We discuss how these technologies have impacted content understanding, conversational recommendation, and user modeling. Finally, we conclude with speculations on the technical challenges surrounding the more general landscape of AI and its implications for supporting personalization for both creators and the audience in search and recommendation.

This talk presents work with many collaborators both past and present at Microsoft, Spotify, and more broadly in academia.

Bio: Paul Bennett is Director of Research on Large Language Models at Spotify. His research focuses on how AI foundational models can improve current creator and audience experiences as well as the role they can play in the next generation of experiences. Previous to joining Spotify, Paul was Partner Research Manager for the Augmented Learning + Reasoning group at Microsoft Research. From 2006-2023 in his roles as a researcher and research manager at Microsoft Research he focused on a broad set of topics at the intersection of Artificial Intelligence and Information Retrieval. His published research has focused on a variety of topics surrounding the use of machine learning in information retrieval – including deep learning for ranking and retrieval, ensemble methods and the combination of information sources, calibration, consensus methods for noisy supervision labels, active learning and evaluation, supervised classification and ranking, crowdsourcing, behavioral modeling and analysis, and personalization. Some of his work has been recognized with awards at SIGIR, CHI, ECIR, and ACM UMAP including a SIGIR Test of Time Award in 2022. Prior to joining industry research labs, he completed his dissertation in the Computer Science Department at Carnegie Mellon with Jaime Carbonell and John Lafferty. While at CMU, he also acted as the Chief Learning Architect on the RADAR project from 2005-2006 while a postdoctoral fellow in the Language Technologies Institute.

Date: Friday, April 12, 2024 - 1:30 - 2:30 PM EDT (North American Eastern Daylight Saving Time) in-person and via Zoom. On campus attendees will gather in CS 142 for the presentation.

Zoom Link: Subscribe to mailing list (details at for Zoom Link/Passcode notifications; or click here for Zoom link and reach out to Hamed Zamani or Alex Taubman for the passcode.