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:

\documentclasssigconf,natbib=true,anonymous=true{acmart}

Submissions must be anonymous and should be submitted electronically.

相关推荐
段一凡-华北理工大学1 小时前
高炉炼铁机器视觉与智能识别十八讲~系列文章11:从单帧到视频流:时序与异常行为识别
人工智能·机器学习·python开发·智能识别·高炉智能化·时序识别
liron712 小时前
技术的诞生与演化--技术自举及其冷启动
人工智能·深度学习·神经网络·自然语言处理
FL16238631293 小时前
外来入侵植物检测数据集VOC+YOLO格式1111张5类别
人工智能·yolo·机器学习
oooost4 小时前
RecFno
人工智能·机器学习
Rocky Ding*5 小时前
VideoChat3 深度解析:视频理解的效率,来自时空压缩与主动感知的共同设计
论文阅读·人工智能·深度学习·机器学习·aigc·ai-native·视频理解
Omics Pro5 小时前
全新可重分析!代谢组质谱专用
数据库·人工智能·算法·机器学习·自然语言处理
忆~遂愿5 小时前
免密连接+快捷键+虚拟鼠标:ToDesk远程操作AI效率拉满
人工智能·python·深度学习·神经网络·自然语言处理·计算机外设·安全架构
ITOM运维行者5 小时前
网络丢包监控怎么做?从六大成因到接口级定位的排查路径
数据库·人工智能·机器学习
敲代码的乔帮主5 小时前
MokioMind 各训练阶段数据样本对比
人工智能·深度学习·机器学习
音视频牛哥6 小时前
人形机器人的未来发展趋势是什么?从VLA、实时视觉到规模化运营
机器学习·机器人·音视频开发