公开(公告)号：US20230153579A1

公开(公告)日：2023-05-18

申请号：US18154523

申请日：2023-01-13

Applicant: HUAWEI TECHNOLOGIES CO., LTD.

Inventor： Jianing SUN ,  Yingxue ZHANG ,  Guo Huifeng ,  Ruiming TANG ,  Xiuqiang HE ,  Dengcheng ZHANG ,  Han YUAN

IPC: G06N3/0464 ,  G06N3/08

CPC classification number: G06N3/0464 ,  G06N3/08

Abstract: Method and system for processing a bipartite graph that comprises a plurality of first nodes of a first node type, and a plurality of second nodes of a second type, comprising: generating a target first node embedding for a target first node based on features of second nodes and first nodes that are within a multi-hop first node neighbourhood of the target first node, the target first node being selected from the plurality of first nodes of the first node type; generating a target second node embedding for a target second node based on features of first nodes and second nodes that are within a multi-hop second node neighbourhood of the target second node, the target second node being selected from the plurality of second nodes of the second node type; and determining a relationship between the target first node and the target second node based on the target first node embedding and the target second node embedding.

公开(公告)号：US20230206076A1

公开(公告)日：2023-06-29

申请号：US18111066

申请日：2023-02-17

Applicant: Huawei Technologies Co., Ltd.

Inventor： Yishi XU ,  Yingxue ZHANG ,  Huifeng GUO ,  Ruiming TANG ,  Yanhui GENG

IPC: G06N3/082 ,  G06N3/045

CPC classification number: G06N3/082 ,  G06N3/045

Abstract: System and method for training a recommender system (RS). The RS is configured to make recommendations in respect of a bipartite graph that comprises a plurality of user nodes, a plurality of item nodes, and an observed graph topology that defines edges connecting at least some of the user nodes to some of the item nodes, the RS including an existing graph neural network (GNN) model configured by an existing set of parameters. The method includes: applying a loss function to compute an updated set of parameters for an updated GNN model that is trained with a new graph using the first set of parameters as initialization parameters, the loss function being configured to distil knowledge based on node embeddings generated by the existing GNN model in respect of an existing graph, wherein the new graph includes a plurality of user nodes and a plurality of item nodes that are also included in the existing graph; and replacing the existing GNN model of the RS with the updated GNN model.