Item Recommendation with Veristic and Possibilistic Metadata: A Preliminary Approach

Item recommendation depends on metadata describing items as well as users through their profiles. Most currently used technologies use precise metadata because of the efficiency of the recommendation process. Nonetheless fuzzy metadata can be useful because of their ability to deal with imprecision and gradedness, two features pervading real-world applications. Fuzzy metadata can have both possibilistic and veristic interpretations, which are complementary and can simultaneously occur in a recommendation context. In this paper we describe a preliminary approach to deal with this double interpretation proposing an extension of the theory of veristic variables, that is specifically suited for item recommendation. Fuzzy metadata are used to calculate the interestingness of an item for a user computing possibility and necessity measures, which enable the ranking of items. As described in the illustrative examples, this approach effectively provides for semantically significant results that are useful for item recommendation with fuzzy metadata.