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.
翻译:随着时装市场的迅速发展,客户对时装建议的需求正在增加。在本文件中,我们的目标是通过回答“我们应选择哪个项目与给定时装项目匹配,并形成一个兼容的服装”的问题来调查时装建议的实际问题。这一问题的关键是估计服装的兼容性。以前的工作侧重于两个项目的兼容性,或以一个序列代表一种服装,但未能充分利用各项目之间的复杂关系。为了纠正这一点,我们提议将一个服装作为图表。特别是,我们建造一个时装图,其中每个节点代表一个类别,每个端点代表两个类别之间的相互作用。因此,每个服饰可以作为子表示,将物品置于相应的时装项目中,并形成一个兼容的服装。为了从这个图表中推断服装的兼容性,我们建议“不偏重图形神经网络”(NGNNN)能够更好地建模节点互动,并学习更好的无偏向表情。在NGNNN中,每个边缘的节点互动是不同的,由两个连接节点的参数所决定。一个注意机制用来计算服装兼容性分数,而不是用来计算高端点的比,我们所学的平面阵列的方法。我们用的是:我们使用的平面阵列的方法。我们所用的格式是比式的比。我们使用的图。我们使用的是:我们使用的图式的图式的比。我们使用的比。我们使用的是:我们使用的图式,我们使用的图式。