公开(公告)号：US20230085684A1

公开(公告)日：2023-03-23

申请号：US17993775

申请日：2022-11-23

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

Inventor： Daxiang DONG ,  Li WANG ,  Jiangwei SUN ,  Zhou XIN

IPC: G06F40/20 ,  G06N3/08 ,  G06F40/30

Abstract: A method of recommending data, a device, and a medium, which relate to a field of an artificial intelligence technology, in particular to fields of deep learning, natural language processing and intelligent recommendation technologies. The method of recommending the data includes: acquiring operation data of an operation object, and the operation data is associated with first content data and first target object data; determining an operation object feature, a content feature and a target object feature based on the operation data; determining a fusion feature based on the operation object feature and the content feature; and recommending second content data and second target object data in an associated manner based on the fusion feature and the target object feature.

公开(公告)号：US20220058222A1

公开(公告)日：2022-02-24

申请号：US17517703

申请日：2021-11-03

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

Inventor： Mo CHENG ,  Dianhai YU ,  Lin MA ,  Zhihua WU ,  Daxiang DONG ,  Wei TANG

IPC: G06F16/901 ,  G06F16/28 ,  G06N5/02

Abstract: The present disclosure provides a method of processing information, an apparatus of processing information, a method of recommending information, an electronic device, and a storage medium. The method includes: obtaining a tree structure parameter of a tree structure, wherein the tree structure is configured to index an object set used for recommendation; obtaining a classifier parameter of a classifier, wherein the classifier is configured to sequentially predict, from a top layer of the tree structure to a bottom layer of the tree structure, a preference node set whose probability of being preferred by a user is ranked higher in each layer, and a preference node set of each layer subsequent to the top layer of the tree structure is determined based on a preference node set of a previous layer of the each layer; and constructing a recalling model based on the tree structure parameter and the classifier parameter.