Abstract
Building on previous work on how to model contextual information for desktop search and how to implement semantically rich information exchange in social networks, we define a new algorithm, Peer-Sensitive ObjectRank for ranking resources on the desktop. The new algorithm takes into account different trust values for each peer, generalizing previous biasing PageRank algorithms. We investigate in detail, how different assumptions about trust distributions influence the ranking of information received from different peers, and which consequences they have with respect to integration of new resources into one peer’s initial network of resources. We also investigate how assumptions concerning size and quality of a peer’s resource network influence ranking after information exchange, and conclude with directions for further research.
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Damian, A., Nejdl, W., Paiu, R. (2005). Peer-Sensitive ObjectRank – Valuing Contextual Information in Social Networks. In: Ngu, A.H.H., Kitsuregawa, M., Neuhold, E.J., Chung, JY., Sheng, Q.Z. (eds) Web Information Systems Engineering – WISE 2005. WISE 2005. Lecture Notes in Computer Science, vol 3806. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11581062_42
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DOI: https://doi.org/10.1007/11581062_42
Publisher Name: Springer, Berlin, Heidelberg
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