Introduction
The rapid advancement of large language models (LLMs) has opened new possibilities for understanding users in search and recommendation. While traditional behavior-based or feature-driven user models rely primarily on explicit interactions or handcrafted representations, LLMs introduce a fundamentally different paradigm: LLM-powered user profiling, where user preferences, intents, and contextual attributes can be extracted, summarized, or reasoned about directly through natural language.
This shift unlocks powerful new paths to achieve personalization but also raises pressing questions related to modeling fidelity, temporal dynamics, evaluation methodology, privacy, and responsible deployment.
The LLM-UP workshop aims to bring together researchers and practitioners to systematize emerging progress in LLM-powered user profiling, identify open challenges, and explore opportunities for integrating such techniques into search and recommendation pipelines.
The LLM-UP workshop adopts an interactive structure featuring lightning talks, panel discussions, and paper presentations to foster active engagement, cross-disciplinary dialogue, and community-driven agenda setting for this rapidly evolving field.
Time Schedule
Workshop Date: Friday 24 July 2026
Venue: TBA
Program
The main focus for the workshop is to provide a venue for researchers and practitioners to get together to exchange ideas and do some consolidation on the emerging progress in LLM-powered user profiling: (The exact time schedule for each part will be announced soon.)
Section 1: Welcome and Opening Remarks (30 mins)
Section 2: Invited Keynote
Section 2.1: Invited Keynote 1: Generative retrieval: from search to recommendation by Prof. Zhaochun Ren (45 mins)
- Abstract: Generative retrieval is reshaping search and recommendation by embedding corpus knowledge directly within generative models. This emerging framework moves beyond traditional indexing, mapping queries and user contexts directly to document or item identifiers. In this talk, I will provide an in-depth overview of generative retrieval with recent key advancements. These methods are bridging the gap between search and recommendation by leveraging semantic representations that capture both content-based and collaborative signals. Building on recent studies, I will outline the core concepts, methodological innovations, and practical applications driving this field, and conclude by discussing open challenges and promising directions for future research.
- Bio: Zhaochun Ren is an Associate Professor at Leiden University, the Netherlands. He is interested in information retrieval and natural language processing, with an emphasis on generative information retrieval and recommender systems. He aims to develop intelligent agents that can address complex user requests and solve core challenges in NLP and IR towards that goal. His research has been recognized with multiple awards at RecSys, SIGIR, WSDM, EMNLP, and CIKM. Prior to joining Leiden, he was a Professor at Shandong University and a Research Scientist at JD.com.
Section 2.2: Invited Keynote 2 by Prof. Tong (Rocky) Chen (45 mins)
Section 2.3: Invited Keynote 3 by Dr. Andrew Drozdov (45 mins)
Section 3: Discussion with SIGIR’26 Authors (40 mins)
Section 4: Wrap-up and Closing Remarks (10 mins)
Workshop Chairs
- Prof. Hongzhi Yin, full professor and ARC Future Fellow at the University of Queensland.
- Dr. Wei Yuan, postdoc at The University of Queensland.
- Mr. Yi Zhang, PhD student at Anhui University.
- Dr. Joel Mackenzie, senior lecturer and DECRA Fellow at The University of Queensland.
- Prof. Quoc Viet Hung Nguyen, associate Professor at Griffith University.
- Prof. Wayne Xin Zhao, full professor at Renmin University of China.
- Prof. Yong Li, full professor at Tsinghua University.
- Prof. Lina Yao, full professor at UNSW.