Patent ID: 11935106
Assignee: BEIJING WODONG TIANJUN INFORMATION TECHNOLOGY CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method for determining a target garment matching an inputted garment, comprising:
providing an attribute extractor comprising a convolutional neural network (CNN);
training, by a computing device, the attribute extractor by using attribute training data, wherein the training comprises:
providing the attribute training data, wherein the attribute training data comprises a plurality of training products, and each training product of the plurality of training products has a text description of the training product, an image of the training product, and an attribute label of the training product;
processing the text description of each of the plurality of training products to obtain a predicted product attribute corresponding to the text description of each of the plurality of training products;
processing, by the CNN, the image of each of the plurality of training products;
converting, by a prediction layer of the CNN, latent vector representations from a CNN result to a predicted product attribute corresponding to the image of each of the plurality of training products; and
training the attribute extractor by using the predicted product attribute corresponding to the text description of each of the plurality of training products, the predicted product attribute corresponding to the image of each of the plurality of training products, and the attribute label of each of the plurality of training products;

receiving, by the computing device, the inputted garment;
extracting, by the trained attribute extractor in the computing device, attributes from a text description and an image of the inputted garment to obtain extracted attributes;
querying, by the computing device, a knowledge graph using the extracted attributes to obtain matched attributes;
retrieving, by the computing device, candidate products from a garment pool using the matched attributes;
extracting, by the computing device, features from the inputted garment and the candidate products;
determining, by the computing device, the target garment from the candidate products based on grading scores between the features of the inputted garment and the features of the candidate products;
pushing, by the computing device, the target garment to a terminal of a user who purchased or reviewed the inputted garment; and
presenting, by the terminal of the user, the target garment to the user,
wherein the knowledge graph comprises nodes corresponding to a type of clothes, a category of clothes, attribute keys comprising sleeve-length, color, pattern, and dress length, values of the attribute keys comprising short-sleeve, red and blue, context keys comprising gender, season and style, values of the context keys comprising woman, summer and casual, a combination of the values of the attribute keys and the type of clothes, and a combination of the values of the attribute keys and the category of clothes,
wherein the knowledge graph comprises edges between the nodes, the edges comprising an “IsA” relationship representing that an attribute of one of the nodes belongs to a category of an attribute of another one of the nodes, a “PartOf” relationship representing that attribution of one of the nodes is part of an attribute of another one of the nodes, and a “Matching_Score” relationship representing compatibility between attribution of two nodes,
wherein matching score values of “Matching_Score” relationships are optimized using one or more garment outfit opinions from key opinion leaders (KOLs), and
wherein a matching score value is a current matching score between a first node and a second node after training using a training outfit, and the current matching score is determined based on a previous matching score between the first node and the second node, a trending index of the first node, and a trending index of the second node.