  In modern fashion e-commerce platforms, where customers can browse thousands
to millions of products, recommender systems are useful tools to navigate and
narrow down the vast assortment. In this scenario, complementary
recommendations serve the user need to find items that can be worn together. In
this paper, we present a personalized, session-based complementary item
recommendation algorithm, ZSF-c, tailored for the fashion usecase. We propose a
sampling strategy adopted to build the training set, which is useful when
existing user interaction data cannot be directly used due to poor quality or
availability. Our proposed approach shows significant improvements in terms of
accuracy compared to the collaborative filtering approach, serving
complementary item recommendations to our customers at the time of the
experiments CF-c. The results show an offline relative uplift of +8.2% in
Orders Recall@5, as well as a significant +3.24% increase in the number of
purchased products measured in an online A/B test carried out in a fashion
e-commerce platform with 28 million active customers.
