Social Sifter: An Agent-Based Recommender System to Mine the Social Web

With the recent growth of the Social Web, an emerging challenge is how we can integrate information from the heterogeneity of current Social Web sites to improve semantic access to the information and knowledge across the entire World Wide Web, the Web. Interoperability across the Social Web sites make the simplest of inferences based on data from different sites challenging. Even if such data were interoperable across multiple Social Web sites, the ability of meaningful inferences of a collective intelligence [1] system depends on both its ability to marshal such semantic data, as well as its ability to accurately understand and precisely respond to queries from its users. This paper presents the architecture for Social Sifter, an agent-based, collective intelligence system for assimilating information and knowledge across the Social Web. A health recommender system prototype was developed using the Social Sifter architecture, which recommends treatments, prevention advice, therapies for ailments, and doctors and hospitals based on shared experiences available on the Social Web.