We present the history-aware transformer (HAT), a transformer-based model that uses shoppers' purchase history to personalise outfit predictions. The aim of this work is to recommend outfits that are internally coherent while matching an individual shopper's style and taste. To achieve this, we stack two transformer models, one that produces outfit representations and another one that processes the history of purchased outfits for a given shopper. We use these models to score an outfit's compatibility in the context of a shopper's preferences as inferred from their previous purchases. During training, the model learns to discriminate between purchased and random outfits using 3 losses: the focal loss for outfit compatibility typically used in the literature, a contrastive loss to bring closer learned outfit embeddings from a shopper's history, and an adaptive margin loss to facilitate learning from weak negatives. Together, these losses enable the model to make personalised recommendations based on a shopper's purchase history. Our experiments on the IQON3000 and Polyvore datasets show that HAT outperforms strong baselines on the outfit Compatibility Prediction (CP) and the Fill In The Blank (FITB) tasks. The model improves AUC for the CP hard task by 15.7% (IQON3000) and 19.4% (Polyvore) compared to previous SOTA results. It further improves accuracy on the FITB hard task by 6.5% and 9.7%, respectively. We provide ablation studies on the personalisation, constrastive loss, and adaptive margin loss that highlight the importance of these modelling choices.

翻译：本文提出历史感知Transformer（HAT），这是一种基于Transformer的模型，利用消费者的购买历史实现个性化穿搭预测。本研究的核心目标是推荐既保持内部协调性，又符合消费者个人风格与偏好的穿搭方案。为实现这一目标，我们采用两级Transformer架构：第一级生成穿搭表征，第二级处理特定消费者的历史购买记录。通过这两个模型，我们能够根据消费者既往购买行为推断其偏好，并以此评估穿搭方案的协调性。在训练过程中，模型通过三重损失函数学习区分已购穿搭与随机组合：文献中常用的穿搭协调性焦点损失、拉近消费者历史穿搭嵌入表征的对比损失，以及促进弱负样本学习的自适应边界损失。这些损失函数共同使模型能够基于购买历史实现个性化推荐。在IQON3000和Polyvore数据集上的实验表明，HAT在穿搭协调性预测（CP）与填空测试（FITB）任务上均优于现有基线模型。相较于先前最优结果，该模型在CP困难任务上将AUC指标提升了15.7%（IQON3000）和19.4%（Polyvore），在FITB困难任务上分别将准确率提高了6.5%和9.7%。我们通过消融实验验证了个性化模块、对比损失与自适应边界损失的设计有效性，凸显了这些建模决策的重要性。