Abstract
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 SemanticSVD + + model. We evaluated several vertex kernels and their effects on recommendation error, and empirically demonstrate the superior performance that we achieve over: (i) existing SVD and SVD + + models; and (ii) SemanticSVD + + with no transferred semantic categories.
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Rowe, M. (2014). Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++. In: Mika, P., et al. The Semantic Web – ISWC 2014. ISWC 2014. Lecture Notes in Computer Science, vol 8796. Springer, Cham. https://doi.org/10.1007/978-3-319-11964-9_22
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DOI: https://doi.org/10.1007/978-3-319-11964-9_22
Publisher Name: Springer, Cham
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