Standard
Transferring semantic categories with vertex kernels: recommendations with SemanticSVD++. /
Rowe, Matthew.
The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. ed. / Peter Mika; Tania Tudorache; Abraham Bernstein; Chris Welty; Craig Knoblock; Denny Vrandečić; Paul Groth; Natasha Noy; Krzysztof Janowicz; Carole Goble. Springer, 2014. p. 341-356 (Lecture Notes in Computer Science; Vol. 8796).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Rowe, M 2014,
Transferring semantic categories with vertex kernels: recommendations with SemanticSVD++. in P Mika, T Tudorache, A Bernstein, C Welty, C Knoblock, D Vrandečić, P Groth, N Noy, K Janowicz & C Goble (eds),
The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. Lecture Notes in Computer Science, vol. 8796, Springer, pp. 341-356, International Semantic Web Conference 2014, Trentino, Italy,
19/10/14.
https://doi.org/10.1007/978-3-319-11964-9_22
APA
Rowe, M. (2014).
Transferring semantic categories with vertex kernels: recommendations with SemanticSVD++. In P. Mika, T. Tudorache, A. Bernstein, C. Welty, C. Knoblock, D. Vrandečić, P. Groth, N. Noy, K. Janowicz, & C. Goble (Eds.),
The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I (pp. 341-356). (Lecture Notes in Computer Science; Vol. 8796). Springer.
https://doi.org/10.1007/978-3-319-11964-9_22
Vancouver
Rowe M.
Transferring semantic categories with vertex kernels: recommendations with SemanticSVD++. In Mika P, Tudorache T, Bernstein A, Welty C, Knoblock C, Vrandečić D, Groth P, Noy N, Janowicz K, Goble C, editors, The Semantic Web – ISWC 2014: 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I. Springer. 2014. p. 341-356. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-11964-9_22
Author
Bibtex
@inproceedings{95584c3f580a464aaa640d01af7c2744,
title = "Transferring semantic categories with vertex kernels: recommendations with SemanticSVD++",
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{\textquoteright}s tastes have evolved beforehand; thereby ignoring if a user{\textquoteright}s preference for a factor is likely to change. One solution to this is to include users{\textquoteright} 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.",
author = "Matthew Rowe",
year = "2014",
doi = "10.1007/978-3-319-11964-9_22",
language = "English",
isbn = "9783319119632",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "341--356",
editor = "Peter Mika and Tania Tudorache and Abraham Bernstein and Welty, {Chris } and Craig Knoblock and Denny Vrande{\v c}i{\'c} and Paul Groth and Natasha Noy and Krzysztof Janowicz and Carole Goble",
booktitle = "The Semantic Web – ISWC 2014",
note = "International Semantic Web Conference 2014 ; Conference date: 19-10-2014 Through 23-10-2014",
}
RIS
TY - GEN
T1 - Transferring semantic categories with vertex kernels
T2 - International Semantic Web Conference 2014
AU - Rowe, Matthew
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-11964-9_22
DO - 10.1007/978-3-319-11964-9_22
M3 - Conference contribution/Paper
SN - 9783319119632
T3 - Lecture Notes in Computer Science
SP - 341
EP - 356
BT - The Semantic Web – ISWC 2014
A2 - Mika, Peter
A2 - Tudorache, Tania
A2 - Bernstein, Abraham
A2 - Welty, Chris
A2 - Knoblock, Craig
A2 - Vrandečić, Denny
A2 - Groth, Paul
A2 - Noy, Natasha
A2 - Janowicz, Krzysztof
A2 - Goble, Carole
PB - Springer
Y2 - 19 October 2014 through 23 October 2014
ER -
