The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.
Expert based recruitment consists of identifying persons with relevant expertise or experience for a given topic. Expert finding have been excessively studied in social networks. @cite_17 developed a Bayesian hierarchical model for expert finding that accounts for both social relationships and content. The model assumes that social links are determined by expertise similarity between candidates. @cite_13 proposed a propagation based approach for finding expert in a social network. The approach consists of two steps. In the first step, they make use of person local information to estimate an initial expert score for each person and select the top ranked persons as candidates. The selected persons are used to construct a sub-graph. In the second step, one’s expert score is propagated to the persons with whom he has relationships.
@cite_13 @cite_17
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This paper addresses the issue of expert finding in a social network. The task of expert finding, as one of the most important research issues in social networks, is aimed at identifying persons with relevant expertise or experience for a given topic. In this paper, we propose a propagation-based approach that takes into consideration of both person local information and network information (e.g. relationships between persons). Experimental results show that our approach can outperform the baseline approach. Expert finding is a task of finding knowledgeable people on a given topic. State-of-the-art expertise retrieval algorithms identify matching experts based on analysis of textual content of documents experts are associated with. While powerful, these models ignore social structure that might be available. In this paper, we develop a Bayesian hierarchical model for expert finding that accounts for both social relationships and content. The model assumes that social links are determined by expertise similarity between candidates. We demonstrate the improved retrieval performance of our model over the baseline on a realistic data set.