Address fuzzy matching method and device

Abstract

The embodiment of the invention provides an address fuzzy matching method and device. The method comprises the following steps: respectively obtaining a plurality of virtual record pairs correspondingto each target user in the standard data block, wherein each standard data block is used for storing the same standard address tables of the first three layers respectively, each virtual record pairis used for storing a corresponding user unique identifier and a corresponding virtual address respectively, and the virtual address is acquired in advance based on detailed address information in a fourth layer of the corresponding standard address table or a layer larger than the fourth layer of the corresponding standard address table; generating a target directed graph by applying each virtualrecord pair; and carrying out message transmission in opposite transmission directions twice in the target directed graph to obtain the address similarity between the virtual record pairs with similarity. According to the method, the efficiency of user address fuzzy matching can be effectively improved, the calculation amount of user address fuzzy matching can be effectively reduced, and then theefficiency of obtaining users with high address similarity by enterprises can be effectively improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to an address fuzzy matching method and device. Background technique [0002] With the development of big data technology, the analysis of social relationships between users has become a common technique and even a necessary means for various enterprises or institutions to analyze user data. For example, when banking institutions identify credit card fraud gangs: the same fraud gang often applies A group of credit cards are used to collect funds. These credit cards often use the same card mailing address, company address, etc. Establishing the relationship between credit card application customers in terms of geographic location information is of great significance for identifying credit card gang fraud. [0003] In the process of each enterprise or institution constructing the social relationship of its users, the establishment of address relationship is very ...

AI Technical Summary

Problems solved by technology

[0005] If the bank has 1 million new address information every day (these address information can come from the customer's transaction address, the company address\residential address filled in when applying for a credit card, etc.), if they are matched one by one: that is, scan the remaining customer addresses for each customer Data to get the address similarity of all other customers, you need to match (1*106)! Second, if the serial execution takes too long to meet the timeliness requirements of bank data analysis

[0006] 2. The matching algorithm is complex:

[0007] No. XX, XX Road, Pudong New District, Shanghai and No. XX, XX Road, Pudong, Shanghai are the same address. If exact matching is used, the relationship cannot be reflected correctly

Therefore, traditionally, various fuzzy matching algorithms are often used for fuzzy matching of addresses. These algorithms are sometimes very complicated, which exacerbates the performance bottleneck of address matching.

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