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Recommender systems anticipate users’ needs by suggesting items that are likely to interest them. Most existing systems employ collaborative filtering (CF) techniques, searching for regularities in the way users have rated items. While in general a successful approach, CF cannot cope well with so-called one-and-only items, that is: items of which there is only one single instance (like an event), and which as such cannot be repetitively “sold”. Typically such items are evaluated only after they have ceased being available, thereby thwarting the classical CF strategy. In this paper, we develop a conceptual framework for recommending one-and-only items. It uses fuzzy logic, which allows to reflect the graded/uncertain information in the domain, and to extend the CF paradigm, overcoming limitations of existing techniques. A possible application in the context of trade exhibition recommendation for e-government is …