Guest Editorial

Various Web systems/services are currently providing a great deal of benefits for users, and Web interaction to design interaction between users and Web systems is becoming especially important both for research and business. Web interaction has been realized through related technologies including interactive data mining/information retrieval, intelligent systems, personalization, user interfaces and so on. However, each study and development has been done independently in different research fields, which might discourage us from studying Web interaction from unified view of human-system interaction and making Web interaction more intelligent by applying machine learning and soft computing. We organized the 2011 International Workshop on Intelligent Web Interaction (IWI-2011) at Lyon to bring together a variety of researchers in diverse fields like Web systems, Artificial Intelligence, computational intelligence, human-computer interaction and user interfaces. The workshop was collocated with 2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI-2011). The IWI workshop has been held from 2006 yearly, and has grown to be one of the largest workshops affiliated with the WI conference. This special issue is consisting of six selected papers from IWI-2011. The purpose of this special issue is to present Intelligent Web Interaction as a new and promising research field. Presenters of the IWI-2011 were encouraged to submit papers to this special issue. All submitted papers are equivalently reviewed in terms of relevance, originality, significance and presentation based on standard review criteria of Journal of Emerging Technologies in Web Intelligence. The first paper “Co-Authorship Networks Visualization System for Supporting Survey of Researchers' Future Activities” (Takeshi Kurosawa and Yasufumi Takama) describes a visualization system to support users to predict future research activities from current co-authorship networks. This is a quite challenging study because it is strongly concerned to prediction of future dynamic development of human-relational networks. Since collaboration of researchers is essential for researchers’ activities, co-authorship network is suitable for predicting future activities. This paper focuses on the task of discriminating growing researchers from supervisors. The effe