Abstract
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 language | English |
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Title of host publication | Proceedings of the RecSys 2017 Workshop on Recommendation in Complex Scenarios (ComplexRec 2017) |
Editors | Toine Bogers, Marijn Koolen, Bamshad Mobasher, Alan Said, Alexander Tuzhilin |
Publisher | CEUR-WS |
Pages | 23-28 |
Number of pages | 5 |
Volume | 1892 |
Publication status | Published - 18 Aug 2017 |
Event | ComplexRec 2017 Workshop: Workshop on Recommendation in Complex Scenarios - Como, Italy Duration: 31 Aug 2017 → 31 Aug 2017 |
Workshop
Workshop | ComplexRec 2017 Workshop |
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Country/Territory | Italy |
City | Como |
Period | 31/08/17 → 31/08/17 |
Keywords
- Package Recommendation
- Matrix Factorization
- Clothes Domain
- Collaborative Filtering