Expertise ranking using activity and contextual link measures

The Internet has transformed from a Web of content to a people-centric Web. People actively use social networking platforms to stay in contact with friends and colleagues. The availability of rich Web-based applications allows people to collaborate and interact online. These connected online societies provide an immense potential for future business models such as crowdsourcing. Based on the idea of crowdsourcing, we developed a framework that enables people to offer their skills and expertise as human-provided services (HPS) which can be discovered and requested on demand. Automated techniques for expertise mining become thus essential in such applications. We introduce a link intensity based ranking model for recommending relevant users in human collaborations. Here we argue that an expertise ranking model must consider the users' availability, activity level, and expected informedness. We present DSARank for estimating the relative importance of persons based on reputation mechanisms in collaboration networks. We test the applicability of our ranking model by using datasets obtained from real human interaction networks including mobile phone and email communications. The results show that DSARank is better suited for recommending users in collaboration networks than traditional degree-based methods.