GEO is IR's next chapter, not a departure from it

SIGIR has always been about connecting people to relevant information — through ranking, relevance modeling, and rigorous evaluation. Generative Engine Optimization (GEO) is the natural extension of that lineage into the LLM era: instead of a ranked list, users now receive a single synthesized, cited, conversational answer; instead of a one-shot query, they engage in an extended, multi-turn conversation in which intent forms and evolves turn by turn.

GUIDE 2026 takes GEO as its entry point into a broader, IR-rooted research agenda.

How do we retrieve, rank, and synthesize information faithfully inside a conversation? How do we track and satisfy user intent as it evolves across turns? How do we orchestrate agents, tools, and recommendation signals to carry a conversation from information-seeking through to a completed action — online or offline? And how do we measure the resulting consumer experience, from relevance and citation quality to satisfaction and NPS?

This agenda is increasingly urgent. LLM-based systems are no longer confined to information service — they now guide product discovery, assist offline purchase decisions in physical retail, and in many cases close the transaction directly, collapsing the boundary between search, recommendation, and commerce. At the same time, rapidly improving LLM memory capabilities open the door to genuinely individual-level personalization, which recommendation techniques — including lightweight adaptation methods such as LoRA — are needed to support at scale. And as LLM systems increasingly compose multiple specialized agent modules to serve a single user journey, recommendation becomes the connective tissue deciding which agent, tool, or piece of content to invoke next: an orchestration problem that is, at its core, an IR and recommendation problem.

A further, cross-cutting need is consumer simulation — building the shared datasets and simulation environments the community needs to study conversation generation and orchestration systematically. Our goal: better consumer experience and agent experience in LLM-native commerce, from information-seeking, to intent fulfillment, to transaction.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome.

Committees

Program Committee

• Haritz Puerto (ELLIS Institute Tübingen),
• Dietmar Jannach (University of Klagenfurt),
• Jiaxin Mao (Renmin University of China),
• Jiqun Liu (University of Wisconsin–Milwaukee),
• Tingshao Zhu (Institute of Psychology, CAS)
• Philipp Christmann (CISPA).

Organizing Committee

• Qiankun ZHAO, PhD

Contact

All questions about submissions should be emailed to qkzhao@guyuai.com