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 47th ACM SIGIR conference, will be run as an in-person
conference from July 14th to 18th, 2024 in Washington D.C., USA. 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 2024 venues will be released separately.
Important Dates for Full Papers
- Time zone: Anywhere on Earth (AoE)
- Full paper abstracts due: January 18, 2024
- Full papers due: January 25, 2024
- Full paper notifications: March 25, 2024
Full paper authors are required to submit an abstract by midnight January 18, 2024 AoE.
Paper submission (deadline: midnight January 25, 2024 AoE) is not possible without a
submitted abstract. We recommend authors waiting for notification from other conferences
to 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”).
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 2024).
Submissions of full research papers must be in English, in PDF format, and be at most 9
pages (including figures, tables, proofs, appendixes, acknowledgments, and any content
except references) in length, 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:
Submissions must be anonymous and should be submitted electronically via EasyChair:
At least one author of each accepted paper is required to register for, 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 2024 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 2024 Pre-Print/ArXiv Policy.
Breaking anonymity or pre-print/ArXiv policy puts the submission at risk of being desk rejected.
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 will be permitted for the camera-ready submission
under any circumstance or after the abstract submission deadline. So please, make sure you
have them listed correctly at submission time.
Desk Rejection Policy
Submissions that violate the anonymity, pre-print policy, length, or
formatting requirements, or are determined to violate
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 extended versions
of the current paper. It is recommended to hold these for the final published version
source code for artifact review.
- 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 include:
Search and Ranking
. Research on core IR algorithmic topics, such as:
System, Efficiency and Scalability
- Queries and query analysis (e.g., query intent, query understanding, query suggestion
and prediction, query representation and reformulation, spoken queries)
- Web search (e.g., ranking of web content, ranking at web scale, link analysis,
sponsored search, search advertising, adversarial search and spam, vertical search)
- Retrieval models and ranking (e.g., ranking algorithms, learning to rank, language models,
retrieval models, combining searches, diversity, aggregated search, dealing with bias)
- Theoretical models and foundations of information retrieval and access (e.g., new theory,
fundamental concepts, theoretical analysis)
. 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
. Research focusing on recommender systems, rich content
representations and content analysis for recommendation, such as:
Machine Learning and Natural Language Processing for IR
- Filtering and recommendation (e.g., content-based filtering, collaborative filtering,
recommender systems, recommendation algorithms, zero-query and implicit search,
- Cross-domain recommendation, socially- and context-aware recommender systems,
- Data characteristics, data quality, and processing challenges underlying recommender
- Novel approaches to recommendation, including voice, VR/AR, etc.
- Preference elicitation, interactive recommender systems
- Other theoretical models and foundations of recommender systems (e.g., economic models)
. Research bridging ML, NLP, and IR.
- Core ML applied to IR, e.g. deep learning for IR, embeddings, reinforcement learning
for IR, learning from noisy/few/biased/problematic IR data, generative AI for IR, etc.
- Large Language Models for IR
- Retrieval Augmented Machine Learning
- Question answering (e.g., factoid and non-factoid question answering, interactive question
answering, community-based question answering, question answering systems)
. 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.
Humans and Interfaces
- End-to-end conversational IR models and optimization
- Modualized IR techniques (e.g., query understanding, user modeling, intent prediction,
context and discourse management, reranking and results presentation)
- 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
. Research into user-centric aspects of IR including user
interfaces, behavior modeling, privacy, interactive systems, such as:
- Mining and modeling users (e.g., user and task models, click models, log analysis,
behavioral analysis, modeling and simulation of information interaction, attention
- Interactive search (e.g., search interfaces, information access, exploratory search,
search context, whole-session support, proactive search, personalized search)
- Social search (e.g., social media search, social tagging, crowdsourcing)
- Collaborative search (e.g., human-in-the-loop, knowledge acquisition)
- Information security (e.g., privacy, surveillance, censorship, encryption, security)
- User studies comparing theory to human behaviour for search and recommendation
. Research that focuses on the measurement and evaluation of IR
systems, such as:
Fairness, Accountability, Transparency, Ethics, and Explainability (FATE) in IR
- User-centered evaluation (e.g., user experience and performance, user engagement,
search task design)
- System-centered evaluation (e.g., evaluation metrics, test collections, experimental
design, evaluation pipelines, crowdsourcing)
- Beyond Cranfield (e.g., online evaluation, task-based, session-based, multi-turn,
- Beyond labels (e.g., simulation, implicit signals, eye-tracking and physiological
- Beyond effectiveness (e.g., value, utility, usefulness, diversity, novelty, urgency,
freshness, credibility, authority)
- Methodology (e.g., statistical methods, reproducibility, dealing with bias, new
experimental approaches, metrics for metrics)
Research on aspects of FATE and bias in search and recommender systems.
Multi Modal IR
- Fairness, accountability, transparency and explainability (e.g. confidentiality,
representativeness, discrimination and harmful bias)
- Ethics, economics, and politics (e.g., studies on broader implications, norms and
ethics, economic value, political impact, social good)
- Two-sided search and recommendation scenarios (e.g. matching users and providers,
. Theoretical, algorithmic or novel practical solutions
addressing problems across the domain of multimedia and IR, such as:
- Multimedia search (e.g., image search, video search, speech and audio search, music
- Multimedia recommendation (e.g., image, video, music recommendations)
- Multimodal for IR (e.g., multimodal IR optimization, user intent prediction,
multimodal personalization, multimodal for collaborative or exploratory algorithms)
. Research focusing on domain-specific IR
challenges, such as:
Other IR Topics
- Local and mobile search (e.g., location-based search, mobile usage understanding, mobile
result presentation, audio and touch interfaces, geographic search, location context
- Social search (e.g., social networks in search, social media in search, blog and
microblog search, forum search)
- Search in structured data (e.g., XML search, graph search, ranking in databases, desktop
search, email search, entity-oriented search)
- Education (e.g., search for educational support, peer matching, info seeking in online
- Legal (e.g., e-discovery, patents, other applications in law)
- Health (e.g., medical, genomics, bioinformatics, other applications in health)
- Other applications and domains (e.g., digital libraries, enterprise, expert search,
news search, app search, archival search, music search, new retrieval problems including
applications of search technology for social good)
. Any IR Research that does not fall into any of the areas above.
For example, but not limited to:
- Explicit semantics (e.g. semantic search, named-entities, relation and event extraction)
- Knowledge acquisition (e.g. information extraction, relation extraction, event extraction,
query understanding, human-in-the-loop knowledge acquisition)
- Knowledge representation and reasoning (e.g., link prediction, knowledge graph completion,
query understanding, knowledge-guided query and document representation, ontology
- Document representation and content analysis (e.g., cross-lingual and multilingual
search, summarization, text representation, linguistic analysis, readability, opinion
mining and sentiment analysis, clustering, classification, topic models for search
AUTHORS TAKE 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.
- Claudia Hauff, Spotify, The Netherlands
- Guido Zuccon, University of Queensland, Australia
- Yi Zhang, University of California Santa Cruz, USA
For any questions about full paper submissions you may contact the Program Chairs by email