Method and device for training model and method and device for generating information

A technology for training models and sub-models, which is applied in the field of training models and can solve problems such as independence

Pending Publication Date: 2021-01-29
BEIJING WODONG TIANJUN INFORMATION TECH CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Another method is to try to use the embedding vector. Although this method can further accurately characterize the user behavior or the characteristics of the predicted object,

Method used

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  • Method and device for training model and method and device for generating information
  • Method and device for training model and method and device for generating information
  • Method and device for training model and method and device for generating information

Examples

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Example Embodiment

[0037]The following describes exemplary embodiments of the present application with reference to the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be regarded as merely exemplary. Therefore, those of ordinary skill in the art should realize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Likewise, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0038]It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other if there is no conflict. Hereinafter, the present application will be described in detail with reference to the drawings and in conjunction with embodiments.

[0039]figure 1 A schematic diagram 100 of the first embodiment of the method for t...

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Abstract

The invention discloses a method and a device for training a model, and a method and a device for generating information. The implementation scheme of the method for training the model comprises the steps of acquiring a training sample set, and a machine learning algorithm being utilized to take behavior feature information, other feature information and object feature information included in training samples in the training sample set as input data; taking a user behavior vector corresponding to the input behavior characteristic information, an object vector corresponding to the input objectcharacteristic information, and preference values between the user corresponding to the other input area characteristic information, the user behavior vector and the object vector and each to-be-predicted object as expected output data; and training to obtain a vector and a user preference generation model. According to the scheme, the user behavior embedding, the to-be-predicted object embeddingand the user portrait preference estimation are carried out at the same time by using one model, so that the prediction of the user preference information has higher accuracy and wider coverage.

Description

technical field [0001] The present application relates to the field of computer technology, specifically to the field of big data technology, and especially to methods and devices for training models, and methods and devices for generating information. Background technique [0002] User portrait is one or more user labels and preference models abstracted based on information such as user identity and behavior. In many fields such as e-commerce, user portraits, as a basic feature of artificial intelligence algorithms, are widely used in various scenarios such as search, recommendation, and advertisement. However, in the face of billions of users, with limited information, how to make the data produced by user portraits more accurate, and how to predict the preferences of users with only a small amount of behavior data, is currently a difficult point in the industry. [0003] At present, the commonly used method is to analyze and predict the preference of the user’s recent be...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06K9/62G06N3/04
CPCG06Q30/0202G06Q30/0201G06N3/049G06F18/214
Inventor 钟灵
Owner BEIJING WODONG TIANJUN INFORMATION TECH CO LTD
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