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

PAKDD 2026

The 30th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) will take place in Hong Kong SAR, China, on June 9-12, 2026. PAKDD 2026 is soliciting contributed technical papers for presentation at the Conference and publication in the Conference Proceedings by Springer. We solicit novel, high-quality, and original research papers that provide innovative insights into all facets of knowledge discovery and data science, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, and integrity. We also encourage visionary papers on emerging topics and application-based papers offering innovative technical advancements to interdisciplinary research and applications of data science.

重要信息

CCF推荐：C（数据库／数据挖掘／内容检索）

录用率：24.1%（168/696，2025年）

时间地点：2026年6月9日-香港·中国

截稿时间：2025年11月15日

Call for Papers - Main Track

Theoretical Foundations

Generative AI, quantum ML, neuro-symbolic methods and reasoning, causal reasoning, non-IID learning, OOD generalization, representation learning, mathematical and statistical foundations, information theoretic approaches, optimization method

Theoretical foundations for fairness, trustworthy AI, safety, model explainability, and XAI

Learning Methods and Algorithms

Clustering, classification, pattern mining and association rules discovery

Supervised learning, semi-supervised learning, few-shot and zero-shot learning, active learning

Reinforcement learning and bandits

Transfer learning, federated learning

Anomaly detection, outlier detection

Learning in recommendation engines

Learning in streams and in time series

Learning on structured data, images, texts and multi-modal data

Online learning, model adaption

Graph mining and Graph NNs

Trustworthy Machine Learning

Fairness

Data Processing for Learning

Dimensionality reduction, feature extraction, subspace construction

Data cleaning and preparation, data integration and summarization

Learning in real-time

Big data technologies

Information retrieval

Data/entity/event/relationship extraction

User interfaces and visual analytics

Security, Privacy, Ethics, Information Integrity and Social Issues

Modeling credibility, trustworthiness, and reliability

Privacy-preserving data mining and privacy models

Model transparency, interpretability, and fairness

Misinformation detection, monitoring, and prevention

Social issues, such as health inequities, social development, and poverty

Interdisciplinary Research on Data Science Applications

Social network/media analysis and dynamics, reputation, influence, trust, opinion mining, sentiment analysis, link prediction, and community detection

Symbiotic human-AI interaction, human-agent collaboration, socially interactive robots, and affective computing

Internet of Things, logistics management, network traffic and log analysis, and supply chain management

Business and financial data, computational advertising, customer relationship management, intrusion and fraud detection, and intelligent assistants

Urban computing, spatial data science and pervasive computing

Medical and public health applications, drug discovery, healthcare management, and epidemic monitoring and prevention

Methods for detecting and combating spamming, trolling, aggression, toxic online behaviors, bullying, hate speech, and low-quality and offensive content

Climate, ecological, and environmental science, and resilience and sustainability

Astronomy and astrophysics, genomics and bioinformatics, high energy physics, robotics, AI-assisted programming, and scientific data

Call for Papers - Survey Track

The PAKDD 2026 Survey Track solicits high-quality survey papers that present a structured synthesis of a particular topic in the area of knowledge discovery, data mining and machine learning, including but not limited to theoretical foundations of mining, inference, and learning, big data technologies, as well as security, privacy, explainability, and integrity.

Call for Papers - Special Track

The PAKDD 2026 Special Track on Large Language Models for Data Science aims to explore the transformative potential of LLMs for Data Science, bringing together researchers, practitioners, and industry experts to discuss the latest developments, challenges, and opportunities in this rapidly growing area.

Novel, high-quality, and original research papers that provide innovative insights into all facets of large language models and their applications in data science, including but not limited to science and algorithms of LLMs, enlarged language models, retrieval-augmented text generation, vision-language pretraining, vision transformers, trustworthiness and societal implications of LLMs, and LLMs on diverse applications are solicited.