中国计算机学会(CCF)推荐学术会议-A(数据库/数据挖掘/内容检索):SIGIR 2026

SIGIR 2026

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 49th ACM SIGIR conference will be run as an in-person conference from July 20 to 24, 2026 in Melbourne | Naarm, Australia.

重要信息

CCF推荐:A(数据库/数据挖掘/内容检索)

录用率:22.3%(239/1071,2025年Full Papers)

时间地点:2026年7月20日-墨尔本·澳大利亚

截稿时间:2026年1月15日

大会官网:https://sigir2026.org/en-AU

Call for Papers

Search and Ranking. Research on core IR algorithmic topics.

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

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

Machine Learning for IR. Research bridging ML and IR.

Natural Language Processing for IR. Research bridging NLP and IR.

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.

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

Datasets, Benchmarks, and Evaluations for IR. Research that focuses on the measurement and evaluation of IR systems.

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

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

Domain-Specific IR Applications. Research focusing on domain-specific IR challenges.

Other IR Topics. Any IR Research that does not fall into any of the areas above.

Submission Guidelines

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:

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

Submissions must be anonymous and should be submitted electronically.

相关推荐
陈天伟教授1 天前
人工智能应用- 天文学家的助手:08. 星系定位与分类
前端·javascript·数据库·人工智能·机器学习
放下华子我只抽RuiKe51 天前
算法的试金石:模型训练、评估与调优的艺术
人工智能·深度学习·算法·机器学习·自然语言处理·数据挖掘·线性回归
renhongxia11 天前
如何对海洋系统进行知识图谱构建?
人工智能·学习·语言模型·自然语言处理·自动化·知识图谱
hsling松子1 天前
基于 PaddleOCR-VL 与 PaddleFormers 的多模态文档解析微调项目
人工智能·计算机视觉·语言模型·自然语言处理·ocr
深圳季连AIgraphX1 天前
UROVAs 端到端自动驾驶模型训练、开闭环测试与上车联调
人工智能·机器学习·自动驾驶
RuiBo_Qiu1 天前
【LLM进阶-后训练&部署】2. 常见的全参数微调SFT方法
人工智能·深度学习·机器学习·ai-native
FluxMelodySun1 天前
机器学习(二十三) 密度聚类与层次聚类
人工智能·机器学习·聚类
鲸鱼在dn1 天前
【CS336】Lecture1课程讲义-语言模型发展历程&Tokenization概念
人工智能·语言模型·自然语言处理
WiSirius1 天前
LLM:基于 AgentScope + Streamlit 的 AI Agent脑暴室
人工智能·深度学习·自然语言处理·大模型·llama
进击ing小白1 天前
OpenCv之图像的仿射和透视变化
人工智能·opencv·机器学习