Leveraging meta-path and co-attention to model consumer preference stability in fashion recommendations

With countless outfit combinations available, consumers often experience choice overload. Two key challenges that significantly impact the quality of recommendation systems are recommendation accuracy and fluctuations in consumer preferences. Previous works primarily extracted generic product features and modeled the compatibility of fashion items, overlooking the relationships hidden in user-product interactions and the evolution of consumer preferences. Unfortunately, this evolution of consumer preferences has not received much attention in the RS studies. To address these limitations, we propose a GPA-BPR (General compatibility and Personalized preference with co-Attention mechanism) framework, which integrates multimodal insights for practical outfit evaluation and utilizes item-user-item meta-paths to capture consumers' stable preferences. Experiments demonstrate significant performance improvements . The co-attention mechanism in our framework effectively enhances recommendations based on meta-path contexts compared to similar previous studies. • Addressed choice overload by focusing on preference stabilization, improving recommendation efficiency. • Personalized outfit recommendations help users reduce search costs and assist decision-making. • Introduced GPA-BPR framework integrating meta-paths and co-attention mechanisms. • Enhanced personalized outfit recommendations with multi-modal data and implicit feedback. • Extensive experiments with real datasets show improved performance of our proposed model.