Federated learning-based recommendation model training method, terminal and storage medium

A model training and federation technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve the problem of poor recommendation model recommendation effect, etc., and achieve the effect of improving recommendation effect and protecting user privacy data.

Pending Publication Date: 2019-10-01
WEBANK (CHINA)
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0003] The main purpose of the present invention is to provide a recommended model training method, terminal and storage medium based on federated learning, aiming to solve

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  • Federated learning-based recommendation model training method, terminal and storage medium
  • Federated learning-based recommendation model training method, terminal and storage medium
  • Federated learning-based recommendation model training method, terminal and storage medium

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Embodiment Construction

[0043] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] The present invention provides a terminal, referring to figure 1 , figure 1 It is a schematic structural diagram of the hardware operating environment involved in the solution of the embodiment of the present invention.

[0045] It should be noted, figure 1 That is, it is a schematic structural diagram of the hardware operating environment of the terminal. The terminal in this embodiment of the present invention may be a PC, or may be a terminal device with a display function such as a smart phone, a smart TV, a tablet computer, or a portable computer.

[0046] Such as figure 1 As shown, the terminal may include: a processor 1001 , such as a CPU, a network interface 1004 , a user interface 1003 , a memory 1005 , and a communication bus 1002 . Wherein, the communication bus 1002 is used to realize connectio...

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Abstract

The invention discloses a federated learning-based recommendation model training method, a terminal and a storage medium. The method comprises the steps of obtaining a user historical behavior data set recorded by a client application in multiple preset types of application projects; extracting a single characteristic user vector of each client application based on each group of user historical behavior data set; extracting a project feature vector set and a project score set from a user historical behavior data set of the target application; combining each single feature user vector, the project feature vector set and the project score set to obtain a local training sample set; and participating in federated learning based on the local training sample set to obtain a recommendation modelof the target type application project. According to the method, model training is carried out under a federal framework to protect user privacy data, meanwhile, recommendation model training is carried out on the basis of multi-scene data, the recommendation model obtained through training can more accurately locate the preference characteristics of the user, and therefore the recommendation effect of the recommendation model is improved.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a federated learning-based recommendation model training method, a terminal and a storage medium. Background technique [0002] With the development of artificial intelligence and machine learning technology, the recommendation system that intelligently recommends music, movies, shopping products and other items for users is also constantly developing. The current recommendation system will upload the user's historical data information to the central database, and gather the data together for the training of the recommendation model. However, uploading user data to the central database will have the risk of leaking user privacy. With the introduction of the concept of federated learning, model training can be carried out under the federated learning architecture, so that users' private data can be combined with multiple clients for effective model training without...

Claims

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Application Information

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IPC IPC(8): G06F16/2457
CPCG06F16/2457
Inventor 黄安埠
Owner WEBANK (CHINA)
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