Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (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 languageEnglish
Title of host publicationProceedings of the 26th ACM Conference on User Modelling, Adaptation and Personalization (UMAP)
Place of PublicationSingapore
PublisherACM
Pages111-119
Number of pages9
ISBN (Print)978-1-4503-5589-6
Publication statusPublished - 2018
EventUser Modeling, Adaptation and Personalization - ACM UMAP 2018: User Modelling, Adaptation and Personalization - Nanyang Technological University, Singapore, Singapore
Duration: 8 Jul 201711 Jul 2018
http://www.um.org/umap2018/ (User Modeling, Adaptation and Personalization - ACM UMAP 2018)

Conference

ConferenceUser Modeling, Adaptation and Personalization - ACM UMAP 2018
CountrySingapore
CitySingapore
Period8/07/1711/07/18
Internet address

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  • Cite this

    Wibowo, A. T., Siddharthan, A., Masthoff, J. F. M., & Lin, C. (2018). Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation. In Proceedings of the 26th ACM Conference on User Modelling, Adaptation and Personalization (UMAP) (pp. 111-119). ACM.