With the rapid development of fashion market, the customers' demands of
customers for fashion recommendation are rising. In this paper, we aim to
investigate a practical problem of fashion recommendation by answering the
question "which item should we select to match with the given fashion items and
form a compatible outfit". The key to this problem is to estimate the outfit
compatibility. Previous works which focus on the compatibility of two items or
represent an outfit as a sequence fail to make full use of the complex
relations among items in an outfit. To remedy this, we propose to represent an
outfit as a graph. In particular, we construct a Fashion Graph, where each node
represents a category and each edge represents interaction between two
categories. Accordingly, each outfit can be represented as a subgraph by
putting items into their corresponding category nodes. To infer the outfit
compatibility from such a graph, we propose Node-wise Graph Neural Networks
(NGNN) which can better model node interactions and learn better node
representations. In NGNN, the node interaction on each edge is different, which
is determined by parameters correlated to the two connected nodes. An attention
mechanism is utilized to calculate the outfit compatibility score with learned
node representations. NGNN can not only be used to model outfit compatibility
from visual or textual modality but also from multiple modalities. We conduct
experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that
matches with existing components of outfit; (2) Compatibility prediction:
predicting the compatibility scores of given outfits. Experimental results
demonstrate the great superiority of our proposed method over others.Comment: 11 pages, accepted by the 2019 World Wide Web Conference (WWW-2019