Abstract:
Embodiments of the present invention provide a method and device for synthesis of network traffic. The method includes extracting a first real traffic composition parameter sequence and a second real traffic composition parameter sequence from real traffic. A first synthetic traffic composition parameter sequence is generated. Relational coefficients between first traffic composition parameters and second traffic composition parameters are obtained. A second synthetic traffic composition parameter sequence is generated and synthetic traffic is generated according to the first synthetic traffic composition parameter sequence and the second synthetic traffic composition parameter sequence.
Abstract:
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.
Abstract:
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.
Abstract:
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≥3, and m>n≥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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
A method for determining a target user, includes: for any target service, acquiring historical data of multiple behavior objects that belong to a same service type as the target service; establishing a correspondence between user identifiers of different users and behavior object identifiers of different behavior objects of the same type; based on multiple established correspondences, constructing a data model that includes a user identifier and a behavior object identifier; using a value update rule to obtain, by means of calculation, a value of a probability that a user corresponding to each user identifier becomes a target user of the target service; and further using the value of the probability to select a target user of the target service, which can not only determine a target user group in a relatively open manner, but also effectively improve accuracy of and efficiency in determining a target user.
Abstract:
Embodiments of the present invention provide a method and device for synthesis of network traffic. The method includes extracting a first real traffic composition parameter sequence and a second real traffic composition parameter sequence from real traffic. A first synthetic traffic composition parameter sequence is generated. Relational coefficients between first traffic composition parameters and second traffic composition parameters are obtained. A second synthetic traffic composition parameter sequence is generated and synthetic traffic is generated according to the first synthetic traffic composition parameter sequence and the second synthetic traffic composition parameter sequence.