GENNEXT-SIGIR-25

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Large Language Models (LLMs) and other generative architectures are rapidly reshaping the fields of Information Retrieval (IR) and Recommender Systems (RS). Advanced language agents—which combine LLMs with specialized tool usage, multi-turn dialogue, and domain knowledge—unlock new capabilities such as context-sensitive retrieval, personalized generation, and adaptive conversation flows.

Despite the potential benefits, these generative models introduce new challenges in terms of hallucination, bias/fairness, data privacy, security, and evaluation methodologies. GENNEXT aims to explore the intersection of LLM-based language agents, generative content creation, and conversational AI in IR and RS—focusing on risks, opportunities, and novel forms of evaluation and user interaction.

Our workshop builds upon the success of the ROEGEN@RecSys'24 event but broadens the scope to include more general information retrieval, next-generation recommendation, and tool-augmented LLMs.

Call for Papers

We invite researchers and practitioners to submit work related (but not limited) to:

  • LLM-driven IR and Recommender Systems
    Prompting, in-context learning, foundation models, or domain-adaptive fine-tuning for IR/RS.
  • Agentic Tool Usage
    Techniques enabling LLMs to call external APIs (e.g., knowledge bases, retrieval models, recommendation engines) to fulfill user queries.
  • Conversational and Dialogue Systems
    Multi-turn interactions, user modeling, dynamic preference elicitation, explanation, or negotiation with generative AI.
  • Generative Content Creation
    Novel item generation (text, images, music), creative item repurposing, or integrated retrieval-plus-generation frameworks.
  • Bias and Fairness, Privacy, and Ethics
    Identifying and mitigating biases in generative models, ensuring privacy, building trust, and tackling hallucination risks.
  • Evaluation Metrics and Benchmarks
    Designing new metrics or user study protocols that capture the interplay of generative quality, recommendation relevance, and ethical concerns.

Submission Guidelines

  • Full Papers: Up to 8 pages (excluding references). Present substantial research, theoretical analyses, or comprehensive surveys.
  • Short Papers: Up to 4 pages (excluding references). Suitable for work-in-progress or preliminary findings.
  • Extended Abstracts: 2–3 pages (excluding references). For late-breaking results, vision papers, or discussion proposals.

Submissions must be anonymized and follow the official ACM two-column SIGCONF template.
All submissions will undergo double-blind peer review.

Accepted papers will be published in CEUR-WS or a similar open-access venue. Selected high-quality submissions may be invited for extension in a journal special issue.

Submission Link: EasyChair for GENNEXT@SIGIR'25

Important Dates (Tentative)

  • Submission Deadline: April 23, 2025 (AOE)
  • Notification of Acceptance: May 21, 2025
  • Camera-Ready Deadline: June 3, 2025
  • Workshop Date: July 17, 2025 (During SIGIR 2025)

Exact deadlines may be adjusted to align with SIGIR final scheduling.

Program (Tentative)

Time Event
09:00-09:15 Opening and Welcome
09:15-10:00 Keynote Talk (Speaker TBA)
10:00-10:30 Paper Presentations (Session I)
10:30-11:00 Coffee Break
11:00-12:15 Paper Presentations (Session II)
12:15-13:30 Lunch Break
13:30-14:15 Keynote Talk (Speaker TBA)
14:15-15:30 Panel Discussion / Breakout Groups
15:30-16:00 Coffee Break
16:00-17:00 Poster/Demo Session
17:00-17:30 Wrap-up and Future Directions

The final schedule will be posted once we confirm the accepted contributions and keynote speakers.

  • Keynote 1: TBA
    Potential topics: bridging LLMs and IR or generative recommendation at scale.
  • Keynote 2: TBA
    Potential topics: addressing fairness, privacy, or hallucinations in generative models.

(Additional speakers to be announced.)

Accepted Papers

A list of accepted papers will be updated here after notifications.

Workshop Organizers

  • Yashar Deldjoo, Tenure-Track Assistant Professor, Polytechnic University of Bari, Italy
  • Julian McAuley, Professor, UC San Diego, USA
  • Scott Sanner, Associate Professor, University of Toronto, Canada
  • Pablo Castells, Professor, Autonomous University of Madrid, Spain
  • Shuai Zhang, Applied Scientist, Amazon Web Services AI, USA
  • Enrico Palumbo, Senior Research Scientist, Spotify
  • Hugues Bouchard, Senior Research Manager, Spotify

Program Committee (Partial List)

  • Michael Ekstrand, Drexel University
  • Craig Boutilier, Google Research
  • Aixin Sun, Nanyang Technological University
  • Jianling Wang, Google DeepMind
  • (Additional members to be announced)

Contact and Further Information

For any inquiries, please email: your-contact-email@example.org

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