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Transferring semantic categories with vertex kernels: recommendations with SemanticSVD++

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date2014
Host publicationThe Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I
EditorsPeter Mika, Tania Tudorache, Abraham Bernstein, Chris Welty, Craig Knoblock, Denny Vrandečić, Paul Groth, Natasha Noy, Krzysztof Janowicz, Carole Goble
PublisherSpringer
Pages341-356
Number of pages16
ISBN (Electronic)9783319119649
ISBN (Print)9783319119632
<mark>Original language</mark>English
EventInternational Semantic Web Conference 2014 - Trentino, Italy
Duration: 19/10/201423/10/2014

Conference

ConferenceInternational Semantic Web Conference 2014
Country/TerritoryItaly
CityTrentino
Period19/10/1423/10/14

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume8796
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Semantic Web Conference 2014
Country/TerritoryItaly
CityTrentino
Period19/10/1423/10/14

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.