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Social-tagging communities offer great potential for smart recommendation and “socially enhanced” searchresult ranking. Beyond traditional forms of collaborative recommendation that are based on the item-user matrix of the entire community, a specific opportunity of social communities is to reflect the different degrees of friendships and mutual trust, in addition to the behavioral similarities among users. This paper presents a framework for harnessing such social relations for search and recommendation. The framework is implemented in the SENSE prototype system, and its usefulness is demonstrated in experiments with an excerpt of the librarything community data.