公开(公告)号：US11802776B2

公开(公告)日：2023-10-31

申请号：US17212251

申请日：2021-03-25

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

Inventor： Hao Liu ,  Panpan Zhang ,  Jianguo Duan ,  Hui Xiong

IPC: G01C21/34 ,  G06Q10/02 ,  G06Q50/14

CPC classification number: G01C21/3484 ,  G01C21/343 ,  G01C21/3423 ,  G06Q10/02 ,  G06Q50/14

Abstract: A cross-regional travel recommendation method and apparatus, an electronic device and a storage medium are provided, which relates to the fields of intelligent transportation and deep learning. A specific implementation solution is: acquiring a travel request of a user, the travel request comprising a start point and an end point which are located in different regions; extracting user features according to the travel request of the user; and recommending at least one travel plan to the user according to the user features and a pre-trained cross-regional travel recommendation model. The technical solutions can make up for the deficiency of the existing technology, provide a cross-regional travel plan recommendation under a large-space scale and a multimodal environment through a pre-trained cross-regional travel recommendation model and user features extracted based on a travel request of a user, and can satisfy a cross-regional travel request of the user, and is highly practical.

公开(公告)号：US20210389148A1

公开(公告)日：2021-12-16

申请号：US17212251

申请日：2021-03-25

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

Inventor： Hao Liu ,  Panpan Zhang ,  Jianguo Duan ,  Hui Xiong

IPC: G01C21/34 ,  G06Q10/02 ,  G06Q50/14

Abstract: A cross-regional travel recommendation method and apparatus, an electronic device and a storage medium are provided, which relates to the fields of intelligent transportation and deep learning. A specific implementation solution is: acquiring a travel request of a user, the travel request comprising a start point and an end point which are located in different regions; extracting user features according to the travel request of the user; and recommending at least one travel plan to the user according to the user features and a pre-trained cross-regional travel recommendation model. The technical solutions can make up for the deficiency of the existing technology, provide a cross-regional travel plan recommendation under a large-space scale and a multimodal environment through a pre-trained cross-regional travel recommendation model and user features extracted based on a travel request of a user, and can satisfy a cross-regional travel request of the user, and is highly practical.