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.