Matrix Factorization for Package Recommendations

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

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

5 Citations (Scopus)
12 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationProceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017)
EditorsToine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin
Number of pages5
Publication statusPublished - 18 Aug 2017
EventComplexRec 2017 Workshop: Workshop on Recommendation in Complex Scenarios - Como, Italy
Duration: 31 Aug 201731 Aug 2017


WorkshopComplexRec 2017 Workshop

Bibliographical note

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.


  • Package Recommendation
  • Matrix Factorization
  • Clothes Domain
  • Collaborative Filtering


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