Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation

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

Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation

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

Open University Milton Keynes

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

8 Citations (Scopus)

Abstract

Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a 'top' (e.g. a shirt) and a 'bottom' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain

Original language	English
Title of host publication	Proceedings of the 26th ACM Conference on User Modelling, Adaptation and Personalization (UMAP)
Place of Publication	Singapore
Publisher	ACM
Pages	111-119
Number of pages	9
ISBN (Print)	978-1-4503-5589-6
Publication status	Published - 2018
Event	User Modeling, Adaptation and Personalization - ACM UMAP 2018: User Modelling, Adaptation and Personalization - Nanyang Technological University, Singapore, Singapore Duration: 8 Jul 2017 → 11 Jul 2018 http://www.um.org/umap2018/ (User Modeling, Adaptation and Personalization - ACM UMAP 2018)

Conference

Conference	User Modeling, Adaptation and Personalization - ACM UMAP 2018
Country/Territory	Singapore
City	Singapore
Period	8/07/17 → 11/07/18
Internet address	http://www.um.org/umap2018/ (User Modeling, Adaptation and Personalization - ACM UMAP 2018)

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. We would also like to thank the participants in our focus groups who communicated precious feedback during our discussions

Cite this

Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation. / Wibowo, Agung Toto; Siddharthan, Advaith; Masthoff, Judith Francoise Maria et al.
Proceedings of the 26th ACM Conference on User Modelling, Adaptation and Personalization (UMAP). Singapore: ACM, 2018. p. 111-119.

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

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title = "Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation",

abstract = "Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a 'top' (e.g. a shirt) and a 'bottom' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain",

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

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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. We would also like to thank the participants in our focus groups who communicated precious feedback during our discussions

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N2 - Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a 'top' (e.g. a shirt) and a 'bottom' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain

AB - Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a 'top' (e.g. a shirt) and a 'bottom' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain

M3 - Published conference contribution

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SP - 111

EP - 119

BT - Proceedings of the 26th ACM Conference on User Modelling, Adaptation and Personalization (UMAP)

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Y2 - 8 July 2017 through 11 July 2018

ER -

Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation

Abstract

Conference

Bibliographical note

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