Abstract: Examples disclosed herein are relevant to systems, methods, and other technology for determining furniture compatibility. For example, graph neural networks (GNNs) that leverage relational information between furniture items in a set may be used as models to predict a compatibility score indicative of visual compatibility of furniture items across the set. In one implementation, the GNN-based model can extend the concept of a siamese network to multiple inputs and branches and use a generalized contrastive loss function. In another implementation, the GNN-based model learns both an edge function and the function that generates the compatibility score. The predicted compatibility score can be used for a variety of purposes, including furniture item recommendations.

Abstract: Examples disclosed herein are relevant to systems, methods, and other technology for determining furniture compatibility. For example, graph neural networks (GNNs) that leverage relational information between furniture items in a set may be used as models to predict a compatibility score indicative of visual compatibility of furniture items across the set. In one implementation, the GNN-based model can extend the concept of a siamese network to multiple inputs and branches and use a generalized contrastive loss function. In another implementation, the GNN-based model learns both an edge function and the function that generates the compatibility score. The predicted compatibility score can be used for a variety of purposes, including furniture item recommendations.