WI 2016 Special Session on

Advanced Methods in Optimization and

Machine Learning for Web Data Mining



Recent advances in storage, hardware, and networking have resulted in a large amount of web data. This has powered the demand to extract useful and actionable insights from such complex and large-scale datasets in an automatic, reliable and effective way. Machine learning, which aims to construct algorithms that can learn from and make predictions on data intelligently, has attracted increasing attention in the recent years and has been successfully applied to many web data mining tasks, such as user behavior modeling, social media computing, online recommendation, link analysis, etc. Since a lot of machine learning algorithms formulate the learning tasks as linear, quadratic or semi-definite mathematical programming problems, optimization becomes a crucial tool and plays a key role in machine learning and web data mining tasks. On the other hand, machine learning and the applications in web data mining are not simply the consumers of optimization technology, but a rapidly evolving interdisciplinary research field that is itself promoting new optimization ideas, models, and solutions.

This special session "Advanced Methods in Optimization and Machine Learning for Web Data Mining" aims to provide a platform for academics and industry-related researchers in the areas of applied mathematics, machine learning, pattern recognition, data mining, knowledge management, network science, social media, and big data to exchange ideas and explore traditional and new areas in optimization and machine learning as well as their applications in webdata mining.

Scope and Topics:

The topics of this special session include, but are not limited to:

Agent and autonomyoriented computation

Cloud-based computing

Clustering and graph-partitioning for web data

Collaborative and content based filtering

Context aware optimization

Cross-media learning

Crowd behavior analysis

Distributed/parallel optimization algorithms in machine learning

EM algorithm and alternating optimization

Feature and subspace selection for web data abstractions

Graph-based learning for web/network data

Human-agent interaction

Implementation issues of optimization and learning in web data mining

Intelligent agents on the web

Learning and adaptation in Multi-agent Systems

Learning complex social networks

Learning for imbalanced web data

Learning for personalization, advertising, and recommendation in web data

Learning for user behavior modeling

Multimedia search and retrieval on web

Multi-objective optimization and many-objective optimization

Non-convex optimization and numerical methods in machine learning

Optimization and machine learning in crowdsourcing

Optimization for large-scale web data

Optimization for mobile computing

Optimization in evolutionary computation

Probabilistic models and graphical models for web data mining

Regularization and generalization in machine learning

Security of web data mining

Sequential learning for video and audio data on the web

Social and economic agents

Social media mining

Sparse coding for web data mining

Supervised/semi-supervised/unsupervised learning for web data mining

Support vector machines and kernel methods for web data mining

Visualization for high-dimensional web data

Online Paper Submission:


Importance Date:

Paper Submission Due Date: June 1, 2016

Paper Notification Date: June 25, 2016


Prof. Yiu-ming Cheung

Department of Computer Science, Hong Kong Baptist University, Hong Kong

E-mail: ymc(at)comp.hkbu.edu.hk


Dr. Yang Liu

Department of Computer Science, Hong Kong Baptist University, Hong Kong

E-mail: csygliu(at)comp.hkbu.edu.hk