Huawei Technologies Co., Limited

Johnson Electric International Ag

Guilin Li

Zhenguo Li

Xiuqiang He

Feng Xue

Bin Liu

Ruiming Tang

Hongguang Li

Huifeng Guo

Chuanjiang Guo

Chuanhui Fang

Shaopeng Mo

Niannan Xue

This application relates to the field of artificial intelligence. A recommendation method based on automatic feature grouping includes: obtaining a plurality of candidate recommended objects and a plurality of association features of each of the plurality of candidate recommended objects; performing multi-order automatic feature grouping on the plurality of association features of each candidate recommended object, to obtain a multi-order feature interaction set of each candidate recommended object; obtaining an interaction feature contribution value of each candidate recommended object through calculation based on the plurality of association features in the multi-order feature interaction set of each candidate recommended object; obtaining a prediction score of each candidate recommended object through calculation based on the interaction feature contribution value of each candidate recommended object; and determining one or more corresponding candidate recommended objects with a high prediction score as a target recommended object.

Recommendation method and apparatus based on automatic feature grouping

An impeller includes a base plate, a hub protruding from the base plate, and a number of blades connected to the base plate and extending from a lateral surface of the hub toward an edge of the base plate. Opposite sides of each of the blades are a working surface and a non-working surface. The base plate is divided into a plurality of sub-plates by the plurality of blades. Each of the sub-plates is located between two adjacent blades and connected to the non-working surface of one of the two adjacent blades and the working surface of the other one of the two adjacent blades. A distance from an edge of each of the sub-plates to a center of rotation of the impeller is varied.

Impeller and water pump having the same

The present invention discloses a network resource recommendation method, including: determining a first interest model according to a user behavior record in a current time window; calculating a similarity between any two interest models in the first interest model, a second interest model, and a third interest model, where a time window corresponding to the second interest model is adjacent to the current time window, the third interest model is determined based on all user behavior records in a reference time window set, the reference time window set includes N time windows prior to the time window corresponding to the second interest model; determining a fourth interest model and a recommendation algorithm according to the calculated similarity between any two interest models; generating a network resource recommendation list according to the fourth interest model and the recommendation algorithm; and making a recommendation according to the network resource recommendation list.

Network resource recommendation method and computer device

Growing community of inventors

Shenzhen, China

Guilin Li