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
OpenBayes贝式计算1 天前
解决视频模型痛点,TurboDiffusion 高效视频扩散生成系统;Google Streetview 涵盖多个国家的街景图像数据集
人工智能·深度学习·机器学习
OpenBayes贝式计算1 天前
OCR教程汇总丨DeepSeek/百度飞桨/华中科大等开源创新技术,实现OCR高精度、本地化部署
人工智能·深度学习·机器学习
够快云库2 天前
能源行业非结构化数据治理实战:从数据沼泽到智能资产
大数据·人工智能·机器学习·企业文件安全
B站_计算机毕业设计之家2 天前
电影知识图谱推荐问答系统 | Python Django系统 Neo4j MySQL Echarts 协同过滤 大数据 人工智能 毕业设计源码(建议收藏)✅
人工智能·python·机器学习·django·毕业设计·echarts·知识图谱
Flying pigs~~2 天前
机器学习之逻辑回归
人工智能·机器学习·数据挖掘·数据分析·逻辑回归
Evand J2 天前
通过matlab实现机器学习的小项目示例(鸢尾花分类)
机器学习·支持向量机·matlab
_Li.2 天前
Simulink - 6DOF (Euler Angles)
人工智能·算法·机器学习·游戏引擎·cocos2d
Project_Observer2 天前
工时日志在项目进度管理中扮演着怎样的角色?
数据库·深度学习·机器学习
scott1985122 天前
Improving Classifier-Free Guidance of Flow Matching via Manifold Projection
人工智能·python·机器学习