公开(公告)号：US20240370928A1

公开(公告)日：2024-11-07

申请号：US18291561

申请日：2021-09-29

Applicant: Beijing BOE Technology Development Co., Ltd. ,  BOE Technology Group Co., Ltd.

Inventor： Boran JIANG ,  Qiong WU ,  Shuqi WEI ,  Chao JI ,  Chuqian ZHONG ,  Ge OU

IPC: G06Q40/06 ,  G06N20/00

Abstract: Disclosed are an asset value evaluation method and apparatus, a model training method and apparatus, and a readable storage medium. The asset value evaluation method includes: acquiring input asset value query information for a user; when it is determined that there is historical asset interaction information of the user, determining an asset set obtained by means of making a query using the asset value query information, the asset set includes at least one asset; performing embedding representation on each asset, so as to determine an asset embedding vector of each asset, the asset embedding vector is obtained by means of training based on the relationship between each asset and an attribute, and the attribute is used for representing an inherent parameter of the asset; and inputting the asset embedding vector of each asset into a graph convolutional network model to obtain the value of each asset for the user.

公开(公告)号：US20240303507A1

公开(公告)日：2024-09-12

申请号：US18026327

申请日：2022-03-30

Applicant: Beijing BOE Technology Development Co., Ltd. ,  BOE Technology Group Co., Ltd.

Inventor： Boran JIANG ,  Ge OU ,  Chao JI ,  Chuqian ZHONG ,  Shuqi WEI ,  Pengfei ZHANG

IPC: G06N5/02 ,  G06Q30/0601

CPC classification number: G06N5/02 ,  G06Q30/0631

Abstract: Provided are a method and device for recommending goods, a method and device for training a goods knowledge graph, and a method and device for training a model. The method for training a goods knowledge graph includes: constructing an initial goods knowledge graph based on a first type of triples and a second type of triples, where a format of the first type of triples is head entity-relation-tail entity, and a format of the second type of triples is entity-attribute-attribute value (S101); and training the initial goods knowledge graph based on a graph embedding model to obtain embedding vectors of entities in the trained goods knowledge graph (S102).