I. Introduction
Identifying and understanding relationships between items is a key component of any modern recommender system. Knowing which items are ‘similar,’ or which otherwise may be substitutable or complementary, is key to building systems that can understand a user's context, recommend alternative items from the same style [10], or generate bundles of items that are compatible [14]–[28].