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Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation

Wibowa, Agung; Siddharthan, Advaith; Masthoff, Judith and Lin, Chenghua (2018). Incorporating Constraints into Matrix Factorization for Clothes Package Recommendation. In: Proceedings of 2018 ACM Conference on User Modeling, Adaptation and Personalization, ACM, New York, pp. 111–119.

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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 The Authors
ISBN: 1-4503-5589-7, 978-1-4503-5589-6
Keywords: recommender systems; package recommendations; matrix factorization
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
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Item ID: 54928
Depositing User: Advaith Siddharthan
Date Deposited: 21 May 2018 08:46
Last Modified: 02 May 2019 01:30
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