Merchant Recommendation Method and System Based on Transition Probability Network

A technology of transition probability and merchant recommendation. It is applied in the field of data processing and can solve problems such as difficulty in finding one's own merchants and no user merchant recommendation.

Active Publication Date: 2022-03-18
CHINA UNIONPAY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, many merchant recommendation functions sort and recommend merchants according to their popularity, which leads to a large number of users receiving similar recommendation information, without making recommendations based on user preferences and merchant characteristics.
In this way, users will still be very confused, and it is difficult to find a merchant that suits them.
Through the search, it is found that some companies now use some machine learning methods to improve the efficiency of merchant recommendation, but most of these solutions are based on the accurate acquisition of various private data of users.

Method used

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  • Merchant Recommendation Method and System Based on Transition Probability Network
  • Merchant Recommendation Method and System Based on Transition Probability Network
  • Merchant Recommendation Method and System Based on Transition Probability Network

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Embodiment Construction

[0042] Introduced below are some of the various embodiments of the invention, intended to provide a basic understanding of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of protection.

[0043] First, the merchant recommendation method based on the transition probability network of the present invention will be described.

[0044] figure 1 is a schematic diagram showing the merchant recommendation method based on the transition probability network of the present invention.

[0045] figure 1 As shown, the merchant recommendation method based on the transition probability network of the present invention includes the following steps:

[0046] OneHot encoding step S100: perform OneHot encoding on N merchants, wherein each merchant is mapped to an N-dimensional sparse vector, and N is a natural number;

[0047] Consumption merchant sequence construction step S200: For a specified account, record and sort the ...

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Abstract

The invention relates to a merchant recommendation method and system based on a transition probability network. The method includes: performing OneHot coding on N merchants, wherein each merchant is mapped to an N-dimensional sparse vector; for a specified account, recording and sorting the merchant codes corresponding to relevant transactions to construct a consumer merchant sequence, wherein the The merchant code is represented by a vector after OneHot encoding; a neural network is constructed, in which the vector after each merchant's OneHot encoding is used as the input layer, and the merchant appearance probability distribution of the merchants that may appear next after the merchant is used as the output layer; and recommending merchants to users based on the merchant appearance probabilities output by the transition probability network construction step. According to the present invention, a transition probability three-layer neural network architecture is proposed, which can quickly analyze the relationship between merchant series and make more accurate recommendations according to the consumption sequence of a large number of users.

Description

technical field [0001] The invention relates to data processing, in particular to a method and system for recommending merchants by constructing a neural network to calculate transition probability. Background technique [0002] When consuming, users are used to first obtaining merchant information from the Internet, and then choose the merchants they are interested in for consumption, even in offline consumption scenarios. Some Internet websites also frequently recommend merchants to users, thereby saving users' shopping time, improving efficiency, and better improving user service experience. [0003] However, many merchant recommendation functions sort and recommend merchants according to their popularity, which leads to a large number of users receiving similar recommendation information, without making recommendations based on user preferences and merchant characteristics. In this way, users still have a lot of confusion, and it is difficult to find a merchant that sui...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06Q30/02G06N3/02
CPCG06N3/02G06Q30/0251G06Q30/0609G06Q30/02G06Q30/0241G06N3/08G06N3/047G06N3/045
Inventor李旭瑞郑建宾赵金涛
OwnerCHINA UNIONPAY