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 9 pages (including figures, tables, proofs, appendixes, acknowledgments, and any content except references) but excluding references. Present substantial research, theoretical analyses, or comprehensive surveys.
Short Papers: Up to 4 pages (including figures, tables, proofs, appendixes, acknowledgments, and any content except references) but 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

Submission Deadline: April 23, 2025 (AOE) New: May 4, 2025 (AOE)
Notification of Acceptance: May 21, 2025
Workshop Date: July 17, 2025 (During SIGIR 2025)
Location: Mantegna 1, Floor 3

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

Program

Time	Event
09:00-09:45	Invited Talk: ChengXiang Zhai (UIUC) "Towards A Unified Agentic Framework for Conversational Information Retrieval and Recommendation: Models, Algorithms, and Evaluation"
09:45-10:30	Contributed Talks: "Resources, Evaluation, RAG, and Information Retrieval" [7 min] "A Resource for Studying Textual Poisoning Attacks against Embedding-based Retrieval-Augmented Generation in Recommender Systems" Fatemeh Nazary, Yashar Deldjoo and Tommaso Di Noia [7 min] "FACap: A Large-scale Fashion Dataset for Fine-grained Composed Image Retrieval" Francois Garderes, Shizhe Chen, Camille-Sovanneary Gauthier and Jean Ponce [7 min] "Exploring Diversity, Novelty, and Popularity Bias in ChatGPT’s Recommendations" Dario Di Palma, Giovanni Maria Biancofiore, Vito Walter Anelli, Fedelucio Narducci and Tommaso Di Noia [3 min] "CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design" Najmeh Forouzandehmehr, Reza Yousefi Maragheh, Sriram Kollipara, Kai Zhao, Topojoy Biswas, Jianpeng Xu, Evren Korpeoglu and Kannan Achan [3 min] "Memory-Aware and Uncertainty-Guided Retrieval for Multi-Hop Question Answering" Yuelyu Ji, Rui Meng, Zhuochun Li and Daqing He [3 min] "Multilingual Information Retrieval with a Monolingual Knowledge Base" Yingyin Zhuang, Aman Gupta and Anurag Beniwal
10:30-11:00	Coffee Break
11:00-11:45	Invited Talk: Julian McAuley (UCSD) "What's still hard about conversational recommendation?"
11:45-12:33	Contributed Talks: "Generative Models for Conversation and Recommendation" [7 min] "Synthetic Dialogue Generation for Interactive Conversational Elicitation & Recommendation (ICER)" Moonkyung Ryu, Chih-Wei Hsu, Yinlam Chow, Mohammad Ghavamzadeh and Craig Boutilier [7 min] "ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation" Reza Yousefi Maragheh, Pratheek Vadla, Priyank Gupta, Kai Zhao, Aysenur Inan, Kehui Yao, Jianpeng Xu, Praveen Kanumala, Jason Cho and Sushant Kumar [7 min] "Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendation" Jiakai Tang, Sunhao Dai, Teng Shi, Jun Xu, Xu Chen, Wen Chen, Jian Wu and Yuning Jiang [3 min] "LLM-based User Profile Management for Recommender System" Seunghwan Bang and Hwanjun Song [3 min] "Personalized Conversational Recommendations via Prompt tuning and Knowledge Injection<" Noriaki Kawamae [3 min] "How Does Multimodal Training Affect Text-Only Recommendation Capabilities of LLMs: A Comparative Analysis" Mert Atay, Ismail Hakki Toroslu, Ismail Sengor Altingovde and Pinar Karagoz [3 min] "Asking Clarifying Questions for Preference Elicitation With Large Language Models" Ali Montazeralghaem, Guy Tennenholtz, Craig Boutilier and Ofer Meshi
12:33-12:40	Wrap-up and Future Directions
13:00-14:30	Joint SIGIR Workshops Poster Session (poster instructions)

Invited Speakers

Keynote 1: ChengXiang Zhai (UIUC)
Title: "Towards A Unified Agentic Framework for Conversational Information Retrieval and Recommendation: Models, Algorithms, and Evaluation"
Abstract: Conversational Information Retrieval (IR) and Recommendation Systems have traditionally been treated as distinct yet closely related tasks. However, recent advancements in large language models (LLMs), interactive user modeling, and reinforcement learning suggest the possibility of viewing both IR and recommender systems as special cases within a broader agentic interaction framework. In this talk, I propose a unified perspective where conversational IR and recommendation tasks are modeled as iterative, cooperative interactions between intelligent agents and users. Key technical challenges and ideas will be discussed, including leveraging LLMs for building general-purpose conversational agents, designing effective memory mechanisms for context retention, modeling diverse user tasks and behaviors, and applying reinforcement learning for adaptive interaction management. Furthermore, I will explore evaluation methodologies, particularly emphasizing the use of simulated user agents to assess the effectiveness and adaptability of these unified conversational agents.
Keynote 2: Julian McAuley (UCSD)
Title: "What's still hard about conversational recommendation?"
Abstract: In this talk we'll explore the current landscape of conversational recommendation in light of new developments on Large Language Models. While LLMs offer a surprisingly effective "off-the-shelf" solution to conversational recommendation, they also have various limitations. We'll look at ways that current models can potentially be improved by exploring new datasets, methods, and evaluation protocols for conversational recommendation.

Workshop Organizers

Yashar Deldjoo, Tenure-Track Assistant Professor, Polytechnic University of Bari, Italy
Julian McAuley, Professor, UC San Diego, USA
Scott Sanner, 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

Yupeng Hou (UCSD)
Rishabh Mehrotra (Sharechat)
Chengkai Huang (University of New South Wales)
Mohamed Reda Bouadjenek (Deakin University)
José Luis Redondo García (Spotify)
Ali Vardasbi (University of Amsterdam)
Martin Mladenov (Google Research)
Branislav Kveton (Adobe Research)
Qidong Liu (City University of Hong Kong)
Azin Ghazimatin (Max Planck Institute)
Gustavo Penha (Spotify)

Contact and Further Information

For any inquiries, please email: gennext_at_sigir2025@googlegroups.com and deldjooy@acm.org