  Embedding based product recommendations have gained popularity in recent
years due to its ability to easily integrate to large-scale systems and
allowing nearest neighbor searches in real-time. The bulk of studies in this
area has predominantly been focused on similar item recommendations. Research
on complementary item recommendations, on the other hand, still remains
considerably under-explored. We define similar items as items that are
interchangeable in terms of their utility and complementary items as items that
serve different purposes, yet are compatible when used with one another. In
this paper, we apply a novel approach to finding complementary items by
leveraging dual embedding representations for products. We demonstrate that the
notion of relatedness discovered in NLP for skip-gram negative sampling (SGNS)
models translates effectively to the concept of complementarity when training
item representations using co-purchase data. Since sparsity of purchase data is
a major challenge in real-world scenarios, we further augment the model using
synthetic samples to extend coverage. This allows the model to provide
complementary recommendations for items that do not share co-purchase data by
leveraging other abundantly available data modalities such as images, text,
clicks etc. We establish the effectiveness of our approach in improving both
coverage and quality of recommendations on real world data for a major online
retail company. We further show the importance of task specific hyperparameter
tuning in training SGNS. Our model is effective yet simple to implement, making
it a great candidate for generating complementary item recommendations at any
e-commerce website.
