Huawei Technologies Co., Limited

Xiuqiang He

Zhenhua Dong

Ruiming Tang

Zhenguo Li

Li Qian

Zhirong Liu

Huifeng Guo

Gong Zhang

Hong Zhu

Guoxiang Cao

Lei Chen

Gang Wang

Fei Chen

Bin Wu

Cheng He

Qiang Yang

Lujia Pan

Bin Liu

Shuaihua Peng

Xiaoguang Li

Yangqiu Song

Guilin Li

Shengyu Zhang

Yanjie Li

Xiangmin Zhou

Andrey Osin

Guohao Cai

Bogdan Cautis

Niannan Xue

Chih Yao Chang

Bowen Yuan

Jieming Zhu

Zhou Zhao

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

Examples of recommendation methods and apparatus are described. In one example method, a plurality of images are obtained, where each image includes one candidate interface and one type of candidate content presented by using the candidate interface. Image feature data of each image is obtained, and input for a prediction model is determined based on user feature data of a target user and the image feature data. Then, a degree of preference of the target user for each image is predicted by using the prediction model. At least one of a candidate interface or candidate content that are included in the plurality of images is selected based on the degree of preference. Recommendation is then performed to the user based on the selected candidate content or candidate interface.

Recommendation method and apparatus, training method and apparatus, device, and recommendation system

A method and an apparatus for training a search recommendation model, and a method and an apparatus for sorting search results are provided. The training method includes: obtaining a training sample set including a sample user behavior group sequence and a masked sample user behavior group sequence; and using the training sample set as input data, and training a search recommendation model, to obtain a trained search recommendation model, where a target of the training is to obtain the object of the response operation of the sample user after the mask processing, the search recommendation model is used to predict a label of a candidate recommendation object in search results corresponding to a query field when a target user inputs the query field, and the label is used to indicate a probability that the target user performs a response operation on the candidate recommendation object.

Method and apparatus for training search recommendation model, and method and apparatus for sorting search results

This application provides an application program (APP) management method, a terminal device, a server, and a system. According to the method, APPs downloaded on a terminal device can be automatically clustered. This saves time of a user and improves user experience. The method is applicable to a terminal device, and the method includes: obtaining a target desktop folder based on type information of an APP downloaded by the terminal device and attribute information of a desktop folder on the terminal device, where the downloaded APP is to be clustered in the target desktop folder; and clustering the downloaded APP into the target desktop folder.

Application program app management method, terminal device, server, and system

This application provides a recommendation model training method in the artificial intelligence (AI) field. The training method includes: obtaining a first training sample; processing attribute information of a first user and information about a first recommended object based on an interpolation model, to obtain an interpolation prediction label of the first training sample; and performing training by using the attribute information of the first user and the information about the first recommended object as an input to a recommendation model and using the interpolation prediction label of the first training sample as a target output value of the recommendation model, to obtain a trained recommendation model. According to the technical solutions of this application, impact of training data bias on recommendation model training can be alleviated, and recommendation model accuracy can be improved.

Recommendation model training method, recommendation method, apparatus, and computer-readable medium

The method includes: collecting historical operations of sample users for M items, and predicting a preference value of a target user for each of the M items according to historical operations of the sample users for each of the M items, collecting classification data of N to-be-recommended items, and classifying the N to-be-recommended items according to the classification data of the N to-be-recommended items, to obtain X themes, where each of the X themes includes at least one of the N to-be-recommended items, and the N to-be-recommended items are some or all of the M items; calculating a preference value of the target user for each of the X themes according to a preference value of the target user for a to-be-recommended item included in each of the X themes; and pushing a target theme to the target user.

Theme recommendation method and apparatus

The method includes: obtaining a plurality of pieces of feature data; automatically performing two different types of nonlinear combination processing operations on the plurality of pieces of feature data to obtain two groups of processed data, where the two groups of processed data include a group of higher-order data and a group of lower-order data, the higher-order data is related to a nonlinear combination of m pieces of feature data in the plurality of pieces of feature data, and the lower-order data is related to a nonlinear combination of n pieces of feature data in the plurality of pieces of feature data, where m&#x2265;3, and m&#x3e;n&#x2265;2; and determining prediction data based on a plurality of pieces of target data, where the plurality of pieces of target data include the two groups of processed data.

Method for data processing by performing different non-linear combination processing

Example prediction methods and apparatus are described. One example includes sending a first model parameter and a second model parameter by a server to a plurality of terminals. The first model parameter and the second model parameter are adapted to a prediction model of the terminal. The server receives a first prediction loss sent by at least one of the plurality of terminals. A first prediction loss sent by each of the at least one terminal is calculated by the terminal based on the prediction model that uses the first model parameter and the second model parameter. The server updates the first model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated first model parameter. The server updates the second model parameter based on the first prediction loss sent by the at least one terminal to obtain an updated second model parameter.

Prediction method, terminal, and server

Method and apparatus of data processing using multiple types of non-linear combination processing

An application sorting method and apparatus are provided. The method includes: obtaining, a positive operation probability and positive operation feedback information of each of at least two data samples; calculating an uncertainty parameter of a positive operation probability of a first data sample based on the positive operation probabilities and the positive operation feedback information of the at least two data samples and feature indication information of at least one same feature in a plurality of features in the at least two data samples; and correcting the positive operation probability of the first data sample by using the uncertainty parameter of the positive operation probability; and sorting, based on corrected positive operation probabilities, application programs corresponding to the at least two data samples.

Application program sorting method and apparatus

A route planning method includes obtaining exercise capability information of a wearer and one or more candidate routes, where the candidate routes include attribute features that comprise historical exercise capability information, where the historical exercise capability information is information calculated according to a first preset rule and based on obtained exercise capability information of a plurality of users having exercised along the candidate routes; determining a target route based on the attribute features of the candidate routes and the exercise capability information of the wearer; and outputting the target route information.

Route planning method and wearable device

An application program recommending method may include acquiring current context information of the terminal, acquiring an amount of context information generated when the terminal runs a first application program, where the first application program refers to an application program stored in the terminal, determining a to-be-used recommending mechanism according to the amount of the context information generated when the terminal runs the first application program, and determining, according to the to-be-used recommending mechanism, a second application program corresponding to the current context information, where the second application program refers to a to-be-recommended application program; and displaying the second application program. In this way, accuracy of predicting an application program to be used by a user is improved. Moreover, when historical information of using an application program by the user is insufficient, a to-be-recommended application program can also be accurately determined.

System and method for recommending applications based on historical usage

A social message monitoring method is implemented by receiving, from a social network server, a social message, and obtaining a theme probability vector of the social message, comparing the theme probability vector of the social message with a theme probability vector of each representative message to obtain a theme similarity, and acquiring a similarity between the social message and each representative message according to the theme similarity, and saving the social message in a message class that contains a representative message most similar to the social message, and outputting the message class to a social network client when a quantity of social messages in the message class reaches a first threshold or themes of social messages in the message class are consistent.

Social message monitoring method and apparatus

A method and a system for processing lifelong learning of a terminal, and an apparatus is presented. The method for processing lifelong learning of a terminal according to the present disclosure includes sending, to a server, a request for downloading a function module, where the download request includes description information of the function module; receiving the function module that is sent by the server and is corresponding to the description information; and using the function module to expand and/or update a local function. According to the embodiments of the present disclosure, the lifelong learning of the terminal is implemented, and a problem in the prior art that the terminal cannot perform function expansion and updating is resolved.

Method and system for processing lifelong learning of terminal and apparatus

The present invention discloses a method and system for recognizing a user activity type, where the method includes: collecting an image of a location in which a user is located; extracting, from the image, characteristic data of an environment in which the user is located and characteristic data of the user; and obtaining, by recognition, an activity type of the user by using an image recognition model related to an activity type or an image library related to an activity type and the characteristic data.

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Shenzhen, China

Xiuqiang He