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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: TBA (During SIGIR 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 1 & 2 (90 mins)

Section 3: Paper Encore (60 mins)

Section 4: Panel Discussion (45 mins)

Section 5: Wrap-up and Closing Remarks (10 mins)


Organisers