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
Godspeed Zhao13 小时前
从零开始学AI16——SVM
算法·机器学习·支持向量机
nebula-AI13 小时前
人工智能导论:模型与算法(核心技术)
人工智能·深度学习·神经网络·算法·机器学习·集成学习·sklearn
AI技术控14 小时前
RAG 怎么做 Query 改写?从工程实践看检索增强生成的第一道关键关卡
人工智能·语言模型·自然语言处理·oracle·nlp
larance16 小时前
[菜鸟教程] 机器学习教程第五课-机器学习如何工作
人工智能·机器学习
哥布林学者16 小时前
深度学习进阶(二十四)Swin 的二维 RPE
机器学习·ai
byzh_rc16 小时前
[自然语言处理-入门] 语音合成
人工智能·自然语言处理
染指111016 小时前
7.相似度计算(本地模型下载和使用,在线模型的使用)-RAG基础1
人工智能·机器学习·阿里云·向量·rag
隐层漫游者17 小时前
2026年了,你还只会sklearn.fit()?手把手教你推导线性回归,深度解析梯度下降与正则化,波士顿房价预测全揭秘!
机器学习
茗创科技18 小时前
Nat Hum Behav | 特征选择会导致基于脑影像的机器学习生物标志物产生迥异的神经生物学解释
python·深度学习·机器学习·matlab·脑网络
财经资讯数据_灵砚智能19 小时前
基于全球经济类多源新闻的NLP情感分析与数据可视化(夜间-次晨)2026年5月18日
人工智能·信息可视化·自然语言处理