  We propose a novel learning framework to answer questions such as "if a user
is purchasing a shirt, what other items will (s)he need with the shirt?" Our
framework learns distributed representations for items from available textual
data, with the learned representations representing items in a latent space
expressing functional complementarity as well similarity. In particular, our
framework places functionally similar items close together in the latent space,
while also placing complementary items closer than non-complementary items, but
farther away than similar items. In this study, we introduce a new dataset of
similar, complementary, and negative items derived from the Amazon co-purchase
dataset. For evaluation purposes, we focus our approach on clothing and fashion
verticals. As per our knowledge, this is the first attempt to learn similar and
complementary relationships simultaneously through just textual title metadata.
Our framework is applicable across a broad set of items in the product catalog
and can generate quality complementary item recommendations at scale.
