Matrix Factorization for Package Recommendations

Agung Toto Wibowo; Advaith Siddharthan; Chenghua Lin; Judith Masthoff

Matrix Factorization for Package Recommendations

Agung Toto Wibowo, Advaith Siddharthan, Chenghua Lin, Judith Masthoff

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

5 Citations (Scopus)

12 Downloads (Pure)

Abstract

Research in recommendation systems has to date focused on recommending individual items to users. However there are contexts in which combinations of items need to be recommended, and there has been less research to date on how collaborative methods such as matrix factorization can be applied to such tasks. The research contributions of this paper are threefold. First, we formalize the collaborative package recommendation task as an extension of the standard collaborative recommendation task. Second, we describe and make available a novel package recommendation dataset in the clothes domain, where a combination of a "top" (e.g. a shirt, t-shirt or top) and "bottom'" (e.g. trousers, shorts or skirts) needs to be recommended. Finally, we describe several extensions of matrix factorization to predict user ratings on packages, and report RMSE improvements over the standard matrix factorization approach for recommending combinations of tops and bottoms.

Original language	English
Title of host publication	Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)
Editors	Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin
Publisher	CEUR-WS
Pages	23-28
Number of pages	5
Volume	1892
Publication status	Published - 18 Aug 2017
Event	ComplexRec 2017 Workshop: Workshop on Recommendation in Complex Scenarios - Como, Italy Duration: 31 Aug 2017 → 31 Aug 2017

Workshop

Workshop	ComplexRec 2017 Workshop
Country/Territory	Italy
City	Como
Period	31/08/17 → 31/08/17

Bibliographical note

ACKNOWLEDGMENTS
We would like to thank Lembaga Pengelola Dana Pendidikan (LPDP), Departemen Keuangan Indonesia for awarding a scholarship to support the studies of the lead author.

Keywords

Package Recommendation
Matrix Factorization
Clothes Domain
Collaborative Filtering

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Matrix Factorization for Package Recommendations
2017 © Copyright for the individual papers remains with the authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. Published on CEUR-WS, Volume 1892.
Final published version, 811 KBLicence: Other

http://ceur-ws.org/Vol-1892/paper5.pdfLicence: Other

Cite this

Matrix Factorization for Package Recommendations. / Wibowo, Agung Toto; Siddharthan, Advaith; Lin, Chenghua et al.
Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). ed. / Toine Bogers; Marijn Koolen; Bamshad Mobasher; Alan Said; Alexander Tuzhilin. Vol. 1892 CEUR-WS, 2017. p. 23-28.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Wibowo, AT, Siddharthan, A, Lin, C & Masthoff, J 2017, Matrix Factorization for Package Recommendations. in T Bogers, M Koolen, B Mobasher, A Said & A Tuzhilin (eds), Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017). vol. 1892, CEUR-WS, pp. 23-28, ComplexRec 2017 Workshop, Como, Italy, 31/08/17. <http://ceur-ws.org/Vol-1892/paper5.pdf>

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abstract = "Research in recommendation systems has to date focused on recommending individual items to users. However there are contexts in which combinations of items need to be recommended, and there has been less research to date on how collaborative methods such as matrix factorization can be applied to such tasks. The research contributions of this paper are threefold. First, we formalize the collaborative package recommendation task as an extension of the standard collaborative recommendation task. Second, we describe and make available a novel package recommendation dataset in the clothes domain, where a combination of a {"}top{"} (e.g. a shirt, t-shirt or top) and {"}bottom'{"} (e.g. trousers, shorts or skirts) needs to be recommended. Finally, we describe several extensions of matrix factorization to predict user ratings on packages, and report RMSE improvements over the standard matrix factorization approach for recommending combinations of tops and bottoms.",

keywords = "Package Recommendation, Matrix Factorization, Clothes Domain, Collaborative Filtering",

author = "Wibowo, {Agung Toto} and Advaith Siddharthan and Chenghua Lin and Judith Masthoff",

note = "ACKNOWLEDGMENTS We would like to thank Lembaga Pengelola Dana Pendidikan (LPDP), Departemen Keuangan Indonesia for awarding a scholarship to support the studies of the lead author.; ComplexRec 2017 Workshop : Workshop on Recommendation in Complex Scenarios ; Conference date: 31-08-2017 Through 31-08-2017",

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AU - Wibowo, Agung Toto

AU - Siddharthan, Advaith

AU - Lin, Chenghua

AU - Masthoff, Judith

N1 - ACKNOWLEDGMENTS We would like to thank Lembaga Pengelola Dana Pendidikan (LPDP), Departemen Keuangan Indonesia for awarding a scholarship to support the studies of the lead author.

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N2 - Research in recommendation systems has to date focused on recommending individual items to users. However there are contexts in which combinations of items need to be recommended, and there has been less research to date on how collaborative methods such as matrix factorization can be applied to such tasks. The research contributions of this paper are threefold. First, we formalize the collaborative package recommendation task as an extension of the standard collaborative recommendation task. Second, we describe and make available a novel package recommendation dataset in the clothes domain, where a combination of a "top" (e.g. a shirt, t-shirt or top) and "bottom'" (e.g. trousers, shorts or skirts) needs to be recommended. Finally, we describe several extensions of matrix factorization to predict user ratings on packages, and report RMSE improvements over the standard matrix factorization approach for recommending combinations of tops and bottoms.

AB - Research in recommendation systems has to date focused on recommending individual items to users. However there are contexts in which combinations of items need to be recommended, and there has been less research to date on how collaborative methods such as matrix factorization can be applied to such tasks. The research contributions of this paper are threefold. First, we formalize the collaborative package recommendation task as an extension of the standard collaborative recommendation task. Second, we describe and make available a novel package recommendation dataset in the clothes domain, where a combination of a "top" (e.g. a shirt, t-shirt or top) and "bottom'" (e.g. trousers, shorts or skirts) needs to be recommended. Finally, we describe several extensions of matrix factorization to predict user ratings on packages, and report RMSE improvements over the standard matrix factorization approach for recommending combinations of tops and bottoms.

KW - Package Recommendation

KW - Matrix Factorization

KW - Clothes Domain

KW - Collaborative Filtering

M3 - Published conference contribution

VL - 1892

SP - 23

EP - 28

BT - Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)

A2 - Bogers, Toine

A2 - Koolen, Marijn

A2 - Mobasher, Bamshad

A2 - Said, Alan

A2 - Tuzhilin, Alexander

PB - CEUR-WS

T2 - ComplexRec 2017 Workshop

Y2 - 31 August 2017 through 31 August 2017

ER -

Matrix Factorization for Package Recommendations

Abstract

Workshop

Bibliographical note

Keywords

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Cite this