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.