  Fashion as characterized by its nature, is driven by style. In this paper, we
propose a method that takes into account the style information to complete a
given set of selected fashion items with a complementary fashion item.
Complementary items are those items that can be worn along with the selected
items according to the style. Addressing this problem facilitates in
automatically generating stylish fashion ensembles leading to a richer shopping
experience for users.
  Recently, there has been a surge of online social websites where fashion
enthusiasts post the outfit of the day and other users can like and comment on
them. These posts contain a gold-mine of information about style. In this
paper, we exploit these posts to train a deep neural network which captures
style in an automated manner. We pose the problem of predicting complementary
fashion items as a sequence to sequence problem where the input is the selected
set of fashion items and the output is a complementary fashion item based on
the style information learned by the model. We use the encoder decoder
architecture to solve this problem of completing the set of fashion items. We
evaluate the goodness of the proposed model through a variety of experiments.
We empirically observe that our proposed model outperforms competitive baseline
like apriori algorithm by ~28 in terms of accuracy for top-1 recommendation to
complete the fashion ensemble. We also perform retrieval based experiments to
understand the ability of the model to learn style and rank the complementary
fashion items and find that using attention in our encoder decoder model helps
in improving the mean reciprocal rank by ~24. Qualitatively we find the
complementary fashion items generated by our proposed model are richer than the
apriori algorithm.
