Résultats de recherche pour « Fashion Knowledge Graph »

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.

Graph theory studies mathematical structures composed of vertices and edges in order to model relationships and connectivity [1, 2]. A MetaGraph is a higher-level construct in which the vertices are themselves graphs, and the edges represent specified relations among those graphs. An Iterated MetaGraph extends this concept recursively: its vertices are MetaGraphs, producing a hierarchy of graph-of-graphs structures across multiple levels. In this work, we explore the possibility of extending Mixed Graphs, DiHyperGraphs, Knowledge Graphs, Intersection Graphs, and Chemical Graphs within the framework of MetaGraphs and Iterated MetaGraphs.