Socially Enhanced Search and Exploration in Social Tagging Networks

Social tagging networks have become highly popular for publishing and searching contents. Users in such networks can review, rate and comment on contents, or annotate them with keywords (social tags) to give short but exact text representations of even non-textual contents. In addition, there is an inherent support for interactions and relationships among users. Thus, users naturally form groups of friends or of common interests. We address three research areas in our work utilising these intrinsic features of social tagging networks. (1) We investigate new approaches for exploiting the social knowledge of and the relationships between users for searching and recommending relevant contents, and integrate them in a comprehensive framework, coined SENSE, for search in social tagging networks. (2) To dynamically update precomputed lists of transitive friends in descending order of their distance in user graphs of social tagging networks, we provide an algorithm for incrementally solving the all pairs shortest distance problem in large, disk-resident graphs and formally prove its correctness. (3) Since users are content providers in social tagging networks, users may keep their own data at independent, local peers that collaborate in a distributed P2P network. We provide an algorithm for such systems to counter cheating of peers in authority computations over social networks. The viability of each solution is demonstrated by extensive experiments regarding effectiveness and efficiency