  Fashion outfit recommendation has attracted increasing attentions from online
shopping services and fashion communities.Distinct from other scenarios (e.g.,
social networking or content sharing) which recommend a single item (e.g., a
friend or picture) to a user, outfit recommendation predicts user preference on
a set of well-matched fashion items.Hence, performing high-quality personalized
outfit recommendation should satisfy two requirements -- 1) the nice
compatibility of fashion items and 2) the consistence with user preference.
However, present works focus mainly on one of the requirements and only
consider either user-outfit or outfit-item relationships, thereby easily
leading to suboptimal representations and limiting the performance. In this
work, we unify two tasks, fashion compatibility modeling and personalized
outfit recommendation. Towards this end, we develop a new framework,
Hierarchical Fashion Graph Network(HFGN), to model relationships among users,
items, and outfits simultaneously. In particular, we construct a hierarchical
structure upon user-outfit interactions and outfit-item mappings. We then get
inspirations from recent graph neural networks, and employ the embedding
propagation on such hierarchical graph, so as to aggregate item information
into an outfit representation, and then refine a user's representation via
his/her historical outfits. Furthermore, we jointly train these two tasks to
optimize these representations. To demonstrate the effectiveness of HFGN, we
conduct extensive experiments on a benchmark dataset, and HFGN achieves
significant improvements over the state-of-the-art compatibility matching
models like NGNN and outfit recommenders like FHN.
