公开(公告)号：US20220114218A1

公开(公告)日：2022-04-14

申请号：US17279377

申请日：2020-06-09

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Tianjian HE ,  Yi LIU ,  Daxiang DONG ,  Yanjun MA ,  Dianhai YU

IPC: G06F16/901 ,  G06F9/30 ,  G06N3/08

Abstract: A session recommendation method, a device and an electronic device are provided, related to the field of graph neural network technology. The session recommendation method includes: acquiring a session control sequence, and acquiring a first embedding vector matrix based on an embedding vector of each of items in the session control sequence; generating a position information sequence based on an arrangement sequence of the items in the session control sequence, and acquiring a second embedding vector matrix based on an embedding vector of each piece of position information in the position information sequence; determining a target embedding vector matrix based on the first embedding vector matrix and the second embedding vector matrix; and determining a recommended item, based on the target embedding vector matrix and through a Session-based Recommendation Graph Neural Network.

公开(公告)号：US20210374356A1

公开(公告)日：2021-12-02

申请号：US17399016

申请日：2021-08-10

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Tianjian HE ,  Yi LIU ,  Daxiang DONG ,  Dianhai YU ,  Yanjun MA

IPC: G06F40/35 ,  G06K9/62

Abstract: The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.