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 |
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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 |
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Country/Territory | Singapore |
City | Singapore |
Period | 8/07/17 → 11/07/18 |
Internet address |
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Bibliographical note
ACKNOWLEDGMENTSWe 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