Matrix Factorisation is a recommendation approach that tries to understand what factors interest a user, based on his past ratings for items (products, movies, songs), and then use this factor information to predict future item ratings. A central limitation of this approach however is that it cannot capture how a user's tastes have evolved beforehand; thereby ignoring if a user's preference for a factor is likely to change. One solution to this is to include users' preferences for semantic (i.e. linked data) categories, however this approach is limited should a user be presented with an item for which he has not rated the semantic categories previously; so called cold-start categories. In this paper we present a method to overcome this limitation by transferring rated semantic categories in place of unrated categories through the use of vertex kernels; and incorporate this into our prior SemanticSV D++ model. We evaluated several vertex kernels and their efects on recommendation error, and empirically demonstrate the superior performance that we achieve over: (i) existing SV D and SV D++ models; and (ii) SemanticSV D++ with no transferred semantic categories.