Personalised Outfit Recommendation via History-aware Transformers
Myong Chol Jung, Julien Monteil, Philip Schulz, Volodymyr Vaskovych

TL;DR
This paper introduces a history-aware transformer model that personalizes outfit recommendations by leveraging shoppers' purchase history, significantly improving compatibility prediction and fill-in-the-blank accuracy over previous methods.
Contribution
The paper proposes a novel transformer-based model that integrates purchase history for personalized outfit recommendations, with new loss functions enhancing learning from weak negatives.
Findings
Outperforms baselines on IQON3000 and Polyvore datasets.
Improves AUC for compatibility prediction by over 15%.
Enhances accuracy on fill-in-the-blank tasks by over 6%.
Abstract
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Manufacturing Process and Optimization · Industrial Vision Systems and Defect Detection
MethodsFocal Loss
