US 11,734,725 B2
Information sending method, apparatus and system, and computer-readable storage medium
Hailin Shi, Beijing (CN); Tao Mei, Beijing (CN); Bowen Zhou, Beijing (CN); He Zhao, Beijing (CN); and Shu Gong, Beijing (CN)
Assigned to BEIJING JINGDONG SHANGKE INFORMATION TECHNOLOGY CO, LTD., Beijing (CN); and BEIJING JINGDONG CENTURY TRADING CO., LTD., Beijing (CN)
Appl. No. 17/54,224
Filed by BEIJING JINGDONG SHANGKE INFORMATION TECHNOLOGY CO, LTD., Beijing (CN); and BEIJING JINGDONG CENTURY TRADING CO., LTD., Beijing (CN)
PCT Filed May 16, 2019, PCT No. PCT/CN2019/087249
§ 371(c)(1), (2) Date Nov. 10, 2020,
PCT Pub. No. WO2019/233258, PCT Pub. Date Dec. 12, 2019.
Claims priority of application No. 201810573612.8 (CN), filed on Jun. 6, 2018.
Prior Publication US 2021/0157869 A1, May 27, 2021
Int. Cl. G06F 16/9535 (2019.01); G06Q 30/0251 (2023.01); G06F 16/9538 (2019.01); G06V 20/40 (2022.01); G06F 18/22 (2023.01); G06V 10/75 (2022.01); G06V 20/52 (2022.01); G06V 40/16 (2022.01)
CPC G06Q 30/0269 (2013.01) [G06F 16/9535 (2019.01); G06F 16/9538 (2019.01); G06F 18/22 (2023.01); G06V 10/75 (2022.01); G06V 20/46 (2022.01); G06V 20/52 (2022.01); G06V 40/169 (2022.01); G06V 40/161 (2022.01); G06V 40/168 (2022.01); G06V 40/174 (2022.01); G06V 40/178 (2022.01)] 20 Claims
OG exemplary drawing
 
1. An information sending method, comprising:
analyzing video data of an offline user to determine an attribute of the offline user;
searching for historical access information of at least one online user matching the attribute of the offline user; and
determining at least one object recommended to the offline user according to the historical access information of each of the at least one online user, and sending information of the at least one object to the offline user,
wherein determining at least one object recommended to the offline user according to the historical access information of each of the at least one online user comprises:
constructing an object recommendation set according to at least one object historically accessed by each of the at least one online user; and
determining the at least one object recommended to the offline user according to a recommendation metric value of each object in the object recommendation set.