PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability
It addresses the need for interpretable and personalized clothing recommendations in the fashion industry, which is incremental as it builds on existing compatibility models by adding attribute-wise interpretability.
The paper tackled the problem of outfit compatibility by developing a personalized recommendation scheme that identifies discordant and harmonious attributes between fashion items, achieving effectiveness verified on the IQON3000 dataset.
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.