Abstract: 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.

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 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 relates to the artificial intelligence field, and in particular, to an item recommendation method and apparatus, and a storage medium. The method includes: obtaining historical interaction data of a target object, where the historical interaction data indicates a historical interaction event between the target object and at least one item; obtaining a pre-trained target recommendation model, where the target recommendation model includes a graph neural network model with one convolutional layer, and the convolutional layer indicates an association relationship between a sample object and a sample item; and invoking, based on the historical interaction data, the target recommendation model to output a target item corresponding to the target object. In the embodiments of this application, a framework structure of the target recommendation model is simplified, so that model operation time is greatly reduced.

Abstract: This application discloses an information recommendation method, which may be applied to the field of artificial intelligence. The method includes: obtaining a target feature vector; and processing the target feature vector by using a recommendation model, to obtain recommendation information, where the recommendation model includes a cross network, a deep network, and a target network; the target network is used to perform fusion processing on a first intermediate output that is output by the first cross layer and a second intermediate output that is output by the first deep layer, to obtain a first fusion result, and the target network is further used to: process the first fusion result to obtain a first weight corresponding to the first cross layer and a second weight corresponding to the first deep layer, and weight the first fusion result with the first weight and the second weight separately.

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: A recommendation device obtains to-be-predicted data and a plurality of target reference samples based on a similarity between the to-be-predicted data and the plurality of reference samples. Each reference sample and the to-be-predicted data each include user feature field data indicating a feature of a target user, and item feature field data indicating a feature of a target item. Each target reference sample and the to-be-predicted data have partially identical user feature field data and/or item feature field data. The recommendation device obtains target feature information of the to-be-predicted data based on the plurality of target reference samples and the to-be-predicted data. The recommendation device then uses the target feature information as input to a deep neural network to obtain a target item that is to be recommended.

Abstract: 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.

Abstract: This application describes a model training method, applied to the field of artificial intelligence. The method includes a computing core of a first processor obtains an embedding used for model training, and writes an updated embedding to a first memory of the first processor instead of transferring the updated embedding to a second processor after model training is completed. In this application, after updating an embedding, the first processor saves the updated embedding to the first memory of the first processor. Without needing to wait for the second processor to complete a process of transferring a second target embedding to a GPU, the first processor may directly obtain the updated embedding and perform model training of a next round based on the updated embedding, provided that the first processor may obtain a latest updated embedding.

Abstract: 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.

Abstract: Embodiments of this application provide a data processing method and apparatus to better learn a vector representation value of each feature value in a continuous feature. The method specifically includes: The data processing apparatus obtains the continuous feature from sample data, and then performs discretization processing on the continuous feature by using a discretization model, to obtain N discretization probabilities corresponding to the continuous feature. The N discretization probabilities correspond to N preset meta-embeddings, and N is an integer greater than 1. Finally, the data processing apparatus determines a vector representation value of the continuous feature based on the N discretization probabilities and the N meta-embeddings.

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: 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.

Abstract: A training method includes: obtaining a first recommendation model, where a model parameter of the first recommendation model is obtained through training based on n first training samples; determining an impact function value of each first training sample with respect to a verification loss of m second training samples in the first recommendation model; determining, based on the impact function value of each first training sample with respect to the verification loss, a weight corresponding to each first training sample; and training the first recommendation model based on the n first training samples and the weights corresponding to the n first training samples, to obtain a target recommendation model.

Abstract: Method and system for processing a bipartite graph that comprises a plurality of first nodes of a first node type, and a plurality of second nodes of a second type, comprising: generating a target first node embedding for a target first node based on features of second nodes and first nodes that are within a multi-hop first node neighbourhood of the target first node, the target first node being selected from the plurality of first nodes of the first node type; generating a target second node embedding for a target second node based on features of first nodes and second nodes that are within a multi-hop second node neighbourhood of the target second node, the target second node being selected from the plurality of second nodes of the second node type; and determining a relationship between the target first node and the target second node based on the target first node embedding and the target second node embedding.

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: 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.

Abstract: 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.

Abstract: This application relates to the field of artificial intelligence.

Abstract: A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.