Recommending fashion items often leverages rich user profiles and makes
targeted suggestions based on past history and previous purchases. In this
paper, we work under the assumption that no prior knowledge is given about a
user. We propose to build a user profile on the fly by integrating user
reactions as we recommend complementary items to compose an outfit. We present
a reinforcement learning agent capable of suggesting appropriate garments and
ingesting user feedback so to improve its recommendations and maximize user
satisfaction. To train such a model, we resort to a proxy model to be able to
simulate having user feedback in the training loop. We experiment on the
IQON3000 fashion dataset and we find that a reinforcement learning-based agent
becomes capable of improving its recommendations by taking into account
personal preferences. Furthermore, such task demonstrated to be hard for
non-reinforcement models, that cannot exploit exploration during training.

通过整合用户反馈建立实时用户画像，并利用强化学习代理提供适宜的服装建议，提高推荐的准确性和用户满意度。

与用户交互的服装推荐

Interactive Garment Recommendation with User in the Loop

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。