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This paper reports initial findings from a study that used quantitative and qualitative research methods and custom–built software to investigate online economies of reputation and user practices in online product reviews at several leading e–commerce sites (primarily Amazon.com).
We describe a network approach to building recommendation systems for a Web service. We employ two different types of weighted graphs in our analysis and development: Proximity graphs, a type of Fuzzy Graphs based on a co-occurrence probability, and semi-metric distance graphs, which do not observe the triangle inequality of Euclidean distances. Both types of graphs are used to develop intelligent recommendation and collaboration systems for the MyLibrary@LANL web service, a user-centered front-end to the Los Alamos National Laboratory's digital library collections and Web resources.