US 2021/0342701 A1 · DEEP LEARNING BASED VISUAL COMPATIBILITY PREDICTION FOR BUNDLE RECOMMENDATIONS

Abstract

Embodiments of the present invention provide systems, methods, and computer storage media for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

Claims20

Dependent claims

One or more computer storage media storing computer-usable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations comprising:

generating, by a first neural network comprising an autoencoder with a graph neural network, a first compatibility score that quantifies compatibility between a partial outfit of items and a candidate item to add to the partial outfit, and is jointly conditioned on item category and item context;
generating, by a second neural network comprising a style autoencoder, a second compatibility score that quantifies visual compatibility between style of the partial outfit and style of the candidate item;
using a learned transformation function to combine the first compatibility score and the second compatibility score into a unified visual compatibility score;
and
performing one or more pre-determined actions based on the unified visual compatibility score.

2 more elements

A computerized method for visual compatibility prediction, the method comprising:

for each of a plurality of candidate items, of a catalog, to fill in a blank in a bundle of items of the catalog, generating a compatibility score quantifying compatibility between the candidate item and the bundle, at least in part by:
representing the items in the bundle as connected nodes in an incomplete graph;
and
using an autoencoder with a graph neural network to predict missing edges in the incomplete graph based on a corresponding adjacency matrix that is weighted by co-occurrence, within the catalog, of categories of items corresponding to pairs of the connected nodes;
and
performing at least one of causing a presentation of a first candidate item having the highest compatibility score or adding the first candidate item to the bundle.

3 more elements

A computer system comprising:

one or more hardware processors and memory configured to provide computer program instructions to the one or more hardware processors;
and
a means for generating a recommendation to add one of a plurality of candidate items from a catalog to an outfit of items from the catalog by using the one or more hardware processors to generate, for each of the candidate items, a visual compatibility score based at least in part on a style embedding for the outfit generated by attending over style embeddings for each item in the outfit.

Dependencies are inferred from the claim text.

Deep learning based visual compatibility prediction for bundle recommendations

Life of this patent

Deep learning based visual compatibility prediction for bundle recommendations

Life of this patent

Abstract

Claims20