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Push model optimization method and device executed by user terminal

A user terminal and user technology, applied in computing models, structured data retrieval, special data processing applications, etc., can solve the problem of user privacy not being protected, and achieve the effect of improving prediction accuracy and protecting private data.

Active Publication Date: 2020-01-17
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Since the current method needs to collect the original information of the user's item interaction (such as click) data on the server side, the user's privacy is not protected

Method used

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  • Push model optimization method and device executed by user terminal
  • Push model optimization method and device executed by user terminal
  • Push model optimization method and device executed by user terminal

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Embodiment Construction

[0030] Embodiments of this specification will be described below with reference to the accompanying drawings.

[0031] figure 1 An object push system 100 according to an embodiment of the specification is shown. like figure 1 As shown in , the system 100 includes a server 11 and a plurality of user terminals 12, wherein each user terminal 12 is connected to the server 11 through a network. In each user terminal 12, a local training push model is included, for example, the push model is a model trained in a specific client (for example, an APP for point-of-interest push) in the terminal, which is used to predict the end user interest in a particular point of interest to determine whether to push that particular point of interest to that user. When the terminal user opens the APP, the APP will determine multiple candidate points of interest for the user based on the geographical location of the terminal user, and input the input features corresponding to the multiple candidat...

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Abstract

The embodiment of the invention provides a push model optimization method and a device executed by a user terminal, the terminal corresponds to a first user, the terminal locally comprises a current push model, the method comprises the steps of acquiring first behavior data of the first user for a first object, and the first object belongs to a predetermined candidate push object set; calculatingfirst gradient data for optimizing the push model based on a first input feature and first behavior data, the first input feature comprising a current feature of the first user obtained from the localand a current feature of a first object obtained from a server; determining a current predetermined number of neighbor users of a first user, the neighbor users having a predetermined association relationship with the first user; and respectively sending the first gradient data to respective terminals of a predetermined number of neighbor users, so that the terminal of each neighbor user respectively optimizes a local push model based on the first gradient data.

Description

technical field [0001] The embodiments of this specification relate to the technical field of machine learning, and more specifically, relate to a method and device for optimizing a push model executed by a user terminal. Background technique [0002] In geographic information systems (such as map APP, navigation APP, etc.), it usually includes multiple points of interest (POI, point of interest). Entities such as schools, banks, restaurants, gas stations, hospitals, supermarkets, etc. The main purpose of the point of interest is to describe the address of things or events, which can greatly enhance the ability to describe and query the location of things or events, and improve the accuracy and speed of geographic positioning. Each POI contains four aspects of information, name, category, coordinates, classification. The current POI recommendation technology is mainly a centralized recommendation system builder, which makes statistics based on the collected original inform...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/9537G06N20/00G06F16/29
CPCG06F16/9536G06F16/9537G06N20/00G06F16/29
Inventor 陈超超王力周俊
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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