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SIGIR 2026: International Conference on Research and Development in Information Retrieval

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Submission Date:
2026-01-15
Notification Date:
2026-04-02
Conference Date:
2026-07-20
Location:
Melbourne | Naarm, Australia
Years:
49
CCF: A   ICORE: A*   QUALIS: A1   Viewed: 114964057   Tracked: 385   Attend: 38

Conference Partner Index (CP-I)

92.4 / 100
Ranked #35 of 5,650 conferences · Top 1%

#4 of 337 in Data Mining & Databases

Academic recognition (35%)
100
Submission selectivity (20%)
88
Editions held (20%)
100
Community attention (10%)
77
Public record completeness (15%)
80

Inputs used: Listed as CCF A, QUALIS A1 · Acceptance rate: 21.7% (mean of 5 editions on file) · Editions on record: 49 · Researchers following it here: 385 · Researchers who opened this page in the past 24 months: 18

Missing from the public record: Past editions (+3.0)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 100% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-05

Call For Papers

SIGIR 2026 (International Conference on Research and Development in Information Retrieval) is a CCF A / ICORE A* / QUALIS A1 conference held in Melbourne | Naarm, Australia on 2026-07-20. The paper submission deadline is 2026-01-15. Acceptance notifications are sent on 2026-04-02.

Relevant topic areas include (but are 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 or Agentic IR. 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 Evaluations for IR. 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 IR 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.
Last updated by Dou Sun on

Acceptance Ratio

Average acceptance rate: 21% over 9 years (2014–2022).

YearSubmittedAcceptedAccepted(%)
202279416120.3%
202172015121%
202055514726.5%
20194268419.7%
20184098621%
20173627821.5%
20163416218.2%
20153517019.9%
20143878221.2%

Best Papers

YearBest Papers
2025WARP: An Efficient Engine for Multi-Vector Retrieval
2024Scaling Laws For Dense Retrieval
2024A Workbench for Autograding Retrieve/Generate Systems
2023The Information Retrieval Experiment Platform
2022A Non-Factoid Question-Answering Taxonomy
2021Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness
2020Controlling Fairness and Bias in Dynamic Learning-to-rank
2019Variance Reduction in Gradient Exploration for Online Learning to Rank
2018Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems
2017BitFunnel: Revisiting Signatures for Search
2016Understanding Information Need: An fMRI Study
2015QuickScorer: A Fast Algorithm to Rank Documents with Additive Ensembles of Regression Trees
2014Partitioned Elias-Fano Indexes
2013Beliefs and Biases in Web Search
2012Time-Based Calibration of Effectiveness Measures
2011Find it if you can: a game for modeling different types of web search success using interaction data
2010Assessing the scenic route: measuring the value of search trails in web logs
2009Sources of evidence for vertical selection
2008Algorithmic mediation for collaborative exploratory search
2007Studying the use of popular destinations to enhance web search interaction
2006Minimal test collections for retrieval evaluation
2006Quantum haystacks
2005Learning to estimate query difficulty: including applications to missing content detection and distributed information retrieval
2004A formal study of information retrieval heuristics
2003Re-examining the potential effectiveness of interactive query expansion
2002Novelty and redundancy detection in adaptive filtering
2001Temporal Summaries of News Topics
2000IR evaluation methods for retrieving highly relevant documents
1999Cross-Language Information Retrieval Based on Parallel Texts and Automatic Mining of Parallel Texts from the Web
1998A Theory of Term Weighting Based on Exploratory Data Analysis
1997Users Lost: Reflections on the Past, Future, and Limits of Information Science (Summary)
1997Feature Selection, Perceptron Learning, and a Usability Case Study for Text Categorization
1996Retrieving Spoken Documents by Combining Multiple Index Sources
1994The Formalism of Probability Theory in IR: A Foundation for An Encumbrance?
1991The Significance of the Cranfield Tests on Index Languages
1988A Look Back and A Look Forward

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