Matrix Factorization for Package Recommendations.

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.