Fashion is an important part of human experience. Events such as interviews,
meetings, marriages, etc. are often based on clothing styles. The rise in the
fashion industry and its effect on social influencing have made outfit
compatibility a need. Thus, it necessitates an outfit compatibility model to
aid people in clothing recommendation. However, due to the highly subjective
nature of compatibility, it is necessary to account for personalization. Our
paper devises an attribute-wise interpretable compatibility scheme with
personal preference modelling which captures user-item interaction along with
general item-item interaction. Our work solves the problem of interpretability
in clothing matching by locating the discordant and harmonious attributes
between fashion items. Extensive experiment results on IQON3000, a publicly
available real-world dataset, verify the effectiveness of the proposed model.

本论文研究了服装搭配的个性化问题，提出了一种基于属性的可解释的搭配方案，并在 IQON3000 数据集上进行了实验证明其有效性。

PAI-BPR：带属性解释的个性化服装推荐方案

PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise  Interpretability

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.

本文提出一种将时装搭配表示为图形，并基于节点间的交互关系通过节点感知的图形神经网络来预测搭配的兼容性得分的方法。实验结果表明，所提出的方法在填空和兼容性预测两个任务上均表现出较高的优越性。