In Brief

The annual SIGIR conference is the major international forum for the presentation of new research results, and the demonstration of new systems and techniques, in the broad field of information retrieval (IR). The 48th ACM SIGIR conference, will be run as an in-person conference from July 13th to 18th, 2025 in Padua, Italy. For the full paper call, we welcome high-impact original papers with contributions related to any aspect of information retrieval and access, including theories, foundations, algorithms, evaluation, analysis, and applications. Please note CFPs for other paper tracks, as well as workshops, tutorials, doctoral consortium, industry day, and other SIGIR 2025 venues will be released separately.

Important Dates for Full Papers

Deadlines time zone: Anywhere on Earth (AoE)

• Full paper abstracts due: January 16, 2025
• Full papers due: January 23, 2025
• Full paper notifications: March 28, 2025

Full paper authors are required to submit an abstract by midnight January 16, 2025 AoE. Paper submission (deadline: midnight January 23, 2025 AoE) is not possible without a submitted abstract. We recommend authors waiting for notification from other conferences should submit an abstract, even if they do not ultimately submit a paper. Immediately after the abstract deadline, PC Chairs will desk reject submissions that lack informative titles and abstracts ("placeholder abstracts").

Submission Guidelines

See this brief checklist to strengthen an IR paper, for authors and reviewers.

Full research papers must describe original work that has not been previously published, not accepted for publication elsewhere, and not simultaneously submitted or currently under review in another journal or conference (including the other tracks of SIGIR 2025).

Submissions of full research papers must be in English, in PDF format, and be at most 9 pages in length (including figures, tables, proofs, appendixes, acknowledgments, and any content except references), with unrestricted space for references, in the current ACM two-column conference format. Suitable LaTeX, Word, and Overleaf templates are available from the ACM Website (use sigconf proceedings template for LaTeX and the Interim Template for Word). ACM’s CCS concepts and keywords are required for review.

For LaTeX, the following should be used:

documentclass[sigconf,natbib=true,anonymous=true]{acmart}

Submissions must be anonymous and should be submitted electronically via EasyChair:

https://easychair.org/conferences/?conf=sigir2025

At least one author of each accepted paper is required to register for SIGIR 2025, and the author(s) or their delegate(s) must present the work at the conference in person.

Anonymity and Pre-Print/ArXiv Policy

The full paper review process is double-blind. Authors are required to take all reasonable steps to preserve the anonymity of their submission. The submission must not include author information; citations or discussion of your own prior work should be written up in third person form. It is acceptable to refer to companies or organizations that provided datasets, hosted experiments or deployed solutions if reviewers can not infer that the authors are currently affiliated with these organizations. You can submit, to SIGIR 2025, papers that you have posted to pre-print/archival platforms (e.g. arXiv), or plan to post in the future, after submission. However, your paper must conform to the SIGIR 2025 Pre-Print/ArXiv Policy. Breaking anonymity or pre-print/ArXiv policy puts the submission at risk of being desk rejected.

Authorship Policy

Authors should carefully go through ACM’s authorship policy before submitting a paper. Please ensure that all authors are clearly identified in EasyChair before the submission deadline. To support the identification of reviewers with conflicts of interest, the full author list must be specified at abstract submission time. No changes to authorship, under any circumstances, will be permitted after the abstract submission deadline or for the camera-ready submission. So please, make sure you have listed authors correctly at abstract submission time.

Use of AI

All submissions must comply with the ACM policy on the use of Artificial Intelligence.

Desk Rejection Policy

Submissions that violate the anonymity, pre-print policy, length, or formatting requirements, or are determined to violate ACM’s policies on academic dishonesty, including plagiarism, author misrepresentation, falsification, etc., are subject to desk rejection by the chairs. Any of the following may result in desk rejection:

• Figures, tables, proofs, appendixes, acknowledgements, or any other content after page 9 of the submission.
• Formatting not in line with the guidelines provided above.
• Authors or authors’ institutional affiliations clearly named or easily discoverable.
• Links to source code repositories that reveal author identities, or links to extended versions of the current paper. It is recommended to hold these for the final published version and submit source code for artifact review. If you need to shared anonymous code, you may use anonymous git repositories, such as https://anonymous.4open.science/.
• Change of authors after the abstract submission deadline.
• Content that has been determined to have been copied from other sources.
• Any form of academic fraud or dishonesty.
• Lack of topical fit for SIGIR.

Relevant Areas

Relevant areas are (but not limited to):

Search and Ranking. Research on core IR algorithmic topics, such as:

• Queries and query analysis
• Web search
• Retrieval models and ranking
• Theoretical models and foundations of information retrieval and access

System, Efficiency and Scalability. Research on search system aspects that relate to the efficiency of the system and/or its scalability, such as:

• Efficient and scalable indexing, crawling, compression, search, and more
• Energy efficiency and green computing for IR
• Search engine architecture, distributed search, metasearch, peer-to-peer search, search in the cloud, edge IR

Recommender Systems. Research focusing on recommender systems, rich content representations and content analysis for recommendation, such as:

• Filtering and recommendation
• Cross-domain recommendation, socially-aware and context-aware recommender systems, multi-stakeholder recommendations
• Novel approaches to recommendation, including voice, VR/AR, etc.
• Other theoretical models and foundations of recommender systems

Machine Learning for IR. Research bridging ML and IR, such as:

• Deep learning for IR
• Reinforcement learning for IR
• Generative IR
• Click models and learning from interactions
• New classification and clustering methods for IR

Natural Language Processing for IR. Research bridging NLP and IR, such as:

• Representation learning for IR
• Large Language Models for IR
• Retrieval Augmented Generation (RAG)
• Question Answering

Conversational IR and Intelligent Agents. Research focusing on developing intelligent IR systems that can understand and respond to users' natural language queries and provide relevant information or recommendations through interactive conversations:

• End-to-end conversational IR models and optimization
• Session based search or recommendation, user engagement
• Conversational question answer, conversational IR for tasks, dialog systems, spoken language interfaces, intelligent chat systems
• Intelligent personal assistants and agents

Humans and Interfaces. Research into user-centric aspects of IR including user interfaces, behavior modeling, privacy, interactive systems, such as:

• User studies, qualitative and quantitative
• User interfaces and visualization
• Social and collaborative search
• User modeling

Datasets, Benchmarks, and Evaluation. Research that focuses on the measurement and evaluation of IR systems, such as:

• Benchmarks and test collections
• User-centered evaluation
• New methods for building data sets
• Online evaluation
• Session-based evaluation
• Simulation for evaluation
• Metrics
• Evaluation methodology

Fairness, Accountability, Transparency, Ethics, and Explainability (FATE) in IR. Research on aspects of FATE and bias in search systems and related applications:

• Fairness, accountability, transparency and explainability
• Ethics, economics, and politics

Multi Modal IR. Theoretical, algorithmic or novel practical solutions addressing problems across the domain of multimedia and IR, such as:

• Multimedia search and retrieval (e.g., image search, video search, speech and audio search, music search)
• Maps and spatial search

Domain-specific Applications. Research focusing on domain-specific IR challenges, such as:

• Local and mobile search
• Social search
• Search in structured data
• Education
• Legal
• Health
• Other applications and domains

Other IR Topics. Any IR Research that does not fall into any of the areas above. For example, but not limited to:

• Explicit semantics
• Information Extraction
• Knowledge acquisition and representation
• Document representation and content analysis
• Information security

AUTHORS PLEASE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of the conference. The official publication date affects the deadline for any patent filings related to published work.

Program Chairs

Omar Alonso,  Amazon, USA

Andrew Trotman,  University of Otago, New Zealand

Suzan Verberne, Leiden University, The Netherlands