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Matrix Factorization for Package Recommendations

Wibowo, Agung Toto; Siddharthan, Advaith; Lin, Chenghua and Masthoff, Judith (2017). Matrix Factorization for Package Recommendations. In: Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017), 1892 pp. 23–28.

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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 Agung Toto Wibowo, 2017 Advaith Siddharthan, 2017 Chenghua Lin, 2017 Judith Masthoff
Keywords: Package Recommendation; Matrix Factorization; Clothes Domain; Collaborative Filtering
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 58720
Depositing User: Advaith Siddharthan
Date Deposited: 23 Jan 2019 10:06
Last Modified: 29 Mar 2019 11:02
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