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SIGIR 2027: International Conference on Research and Development in Information Retrieval

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投稿締切日:
2027-01-14 残り 97 日
通知日:
2027-04-05
開催日:
2027-07-18
開催地:
San Jose, California, USA
開催回数:
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CCF: A   ICORE: A*   QUALIS: A1   閲覧: 114966865   フォロー: 385   参加: 38
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会伴インデックス (CP-I)

95.6 / 100
全 5,694 件中 第 12 位 · 上位 1%

データマイニング・データベース 分野 336 件中 第 3 位

学術的評価 (35%)
100
投稿の選択性 (20%)
88
開催回数 (20%)
100
コミュニティの注目度 (10%)
79
公開情報の充実度 (15%)
100

使用した入力: 収録ランク：CCF A, QUALIS A1 · 採択率：21.7%（記録のある 5 回の平均） · 確認できる開催回数：50 · 会伴でフォローしている研究者：385 人 · 過去 24 か月にこのページを開いた研究者：24 人

信頼度 100% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-10-09

論文募集

SIGIR 2027 (International Conference on Research and Development in Information Retrieval) is a CCF A / ICORE A* / QUALIS A1 conference held in San Jose, California, USA on 2027-07-18. The paper submission deadline is 2027-01-14. Acceptance notifications are sent on 2027-04-05.

New in 2027 This year's Full Paper CFP introduces several changes from prior years. Here is a summary: Agentic IR is now a standalone topic area. Reflecting the rapid growth of agent-based information access, from tool-augmented retrieval to autonomous multi-step search, we have elevated Agentic IR into its own top-level area, separate from Conversational IR. New topic area: Information Access through Generative AI. We introduce a dedicated area for research on how generative models are changing the way people access information, covering attribution, grounding, retrieval integration in compound AI systems, and the interplay between retrieval-based and generation-based access. Expanded FATE area to include AI Safety in IR. The Fairness, Accountability, Transparency, Ethics area now explicitly covers adversarial robustness, misinformation in AI-mediated search, guardrails for agentic systems, and alignment of search with user intent. Consolidated "Foundation Models for IR" area. We merged the former "Machine Learning for IR" and "Natural Language Processing for IR" areas into a single area reflecting the convergence of these fields around foundation models. The emphasis is on models that advance retrieval and access, not on general model research evaluated on IR benchmarks. Updated evaluation area. We added explicit encouragement for evaluation frameworks addressing agentic and multi-step IR, groundedness and hallucination measurement, and cost-quality tradeoffs. Chair for Integrity and Robustness. We are introducing a dedicated chair role to support the review process with automated checks on research integrity and methodological robustness. This complements and does not replace expert reviewer judgment. Satellite presentation option. Presenting authors who are unable to obtain a visa may apply to present at a satellite location instead. Requests are reviewed through an approval process. Please check Satellite Presentation Policy for eligibility and how to apply. Relevant Areas 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. Inference cost optimization for neural retrieval and generative components. 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. LLM-based and conversational recommendation. Recommendation in agentic workflows and compound AI systems. Other theoretical models and foundations of recommender systems. Foundation Models for IR (formerly "Machine Learning for IR" and "NLP for IR") Research that uses or adapts foundation models to advance information retrieval and access. We emphasize contributions where the retrieval or access problem drives the research, rather than general model papers evaluated on IR benchmarks. Topics include: Deep learning and representation learning for IR. Fine-tuning, distillation, and adaptation of foundation models for retrieval. Generative retrieval models (e.g., generative document IDs, direct answer generation with attribution). Retrieval Augmented Generation (RAG) and retrieval-augmented reasoning. Reasoning-augmented retrieval (e.g., chain-of-thought for search, inference-time compute scaling). Reinforcement learning and learning from interactions for IR. Click models and implicit feedback. Question answering. Agentic IR (new — expanded from "Conversational or Agentic IR") Research on IR systems that autonomously plan, use tools, and execute multi-step information tasks with minimal user intervention: Tool-augmented and API-augmented retrieval. Multi-step search planning and execution. Autonomous web agents for information tasks. Agentic RAG, including orchestrating retrieval, reasoning, and action. Intelligent personal assistants and agents. Conversational IR Research on interactive, user-driven information access through multi-turn dialog: End-to-end conversational IR models and optimization. Session-based search and recommendation, user engagement. Conversational question answering, dialog systems, spoken language interfaces. Mixed-initiative interaction, when the system and user jointly steer the search process. Information Access through Generative AI (new) Research on how generative models are changing the way people find and access information, such as: Attribution, grounding, and source provenance in generative search. Retrieval as the grounding layer in generative AI systems. User information-seeking behavior in the era of answer engines (e.g., zero-click search, changing query patterns). Retrieval quality, ranking robustness, and credibility assessment in corpora with AI-generated content. Comparison and complementarity of retrieval-based and generation-based information access. 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. Human-AI collaboration in IR and sensemaking. Datasets, Benchmarks, and Evaluation 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. Evaluation frameworks for agentic and multi-step IR. Measuring groundedness, hallucination, and factual accuracy in generative retrieval. Evaluation of agentic IR systems (correctness, safety, cost, latency). Simulation for evaluation. Metrics and evaluation methodology. Fairness, Accountability, Transparency, Ethics, Safety, and Explainability (FATES) in IR (expanded) Research on FATES aspects and bias in search systems and related applications: Fairness, accountability, transparency and explainability. Ethics, economics, and politics. Adversarial robustness of retrieval-augmented and agentic systems. Misinformation, manipulation, and content integrity in AI-mediated search. Guardrails, alignment, and safety in agentic IR. 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. Multi-modal embeddings and cross-modal retrieval. 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. Scientific literature search and discovery. 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: Information extraction and knowledge representation. Document representation and content analysis. Information security.
最終更新：Admin Agent （）

採択率

平均採択率: 21% 9 年間 (2014–2022).

年投稿数採択数採択率(%)
202279416120.3%
202172015121%
202055514726.5%
20194268419.7%
20184098621%
20173627821.5%
20163416218.2%
20153517019.9%
20143878221.2%

ベストペーパー

年ベストペーパー
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

これを見た人はこちらも見ています

CCFICORECP-I略称正式名称投稿締切開催日
AA*97.0The Web ConferenceThe ACM Web Conference2026-10-182027-05-10
AA*96.2ACLAnnual Meeting of the Association for Computational Linguistics2027-01-042027-08-17
AA*89.4ICCVInternational Conference on Computer Vision2025-03-072025-10-19
AA*81.4SIGMODACM Conference on Management of Data2026-10-102027-06-13
BA*79.7PODSACM SIGMOD Conference on Principles of DB Systems2026-12-032027-06-13
BA*94.2IJCAIInternational Joint Conference on Artificial Intelligence2026-01-312026-08-15
AA*94.5CVPRIEEE Conference on Computer Vision and Pattern Recognition2026-11-102027-06-20
BA*89.1ECCVEuropean Conference on Computer Vision2026-03-062026-09-08
BA89.9WSDMInternational Conference on Web Search and Data Mining2026-08-172027-02-15
AA*89.4ICLRInternational Conference on Learning Representations2026-09-182027-04-26

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