Approximating matching method for numerous character strings

Abstract

The invention discloses an approximating matching method for numerous character strings, which comprises the steps of: (1) choosing a main matching parameter of an object to be matched, (2) adjusting the weighted value of the parameter, (3) constructing a many-to-many matching module by utilizing stable marriage asymmetric algorithm, (4) creating an optimized list according to the editing distance algorithm or the longest common sub-sequence, aiming at the matching items in the many-to-many matching module. Basing on an algorithm module of main body, the invention automatically chooses the algorithm by adding attributive analysis. After constructing the module, the matching result is stable, the matching rate is high, and the matching is real-time and rapid. According to different application scenes, the optimized list can be created by utilizing different approximating matching algorithms for different character strings. With excellent application prospect, the pertinent product strategy can be made in a short time, the product competitiveness can be enhanced, the running efficiency of website can be promoted, and the system performance can be improved.

Description

Technical field: [0001] The invention relates to a product data matching method, in particular to an approximate matching method of a large number of character strings applied in electronic commerce. Background technique: [0002] With the rapid development of e-commerce, the competition of e-commerce websites based on the B2C model is also intensifying, and its core is reflected in the differences in prices, activities and services among each product sold. Therefore, it is imperative to understand the various data of external website products in real time every day, and then make correct sales strategies and improve competitiveness. [0003] There are two algorithms currently used in the approximate matching method of products in the field of e-commerce: [0004] 1. Edit distance algorithm: [0005] It is used to judge the similarity between strings, which is equal to the minimum cost required to transform one string into another string through basic transformation. Edit...

AI Technical Summary

Problems solved by technology

[0010] Disadvantages: Only the similarity between two strings can be calculated, and the length of a substring or subsequence in which one string is another string cannot be obtained

[0020] Disadvantage: The calculated longest common subsequence string is discontinuous and does not support backtracking

[0023] Disadvantages: It is necessary to construct the sequence of each person's preferred objects in advance, such as in e-commerce product matching, lacking an algorithm for constructing a preferred list

[0024] Therefore, at present, all medium and large e-commerce websites will use excellent web page information tracking or crawler tool software to capture external product data, but it has certain prerequisites and limitations:

[0025] 1. Some products on external websites lack key attributes or specifications as a direct comparison basis for matching and grabbing; for example, the product "Creative ZEN 4GB BLACK Mp3Mp4Video Player with Expandable SD Card Slot", which is a product of a B2C website However, the product naming method of each B2C website is different, so if the product number is missing in the information list, the accuracy of matching crawling will decrease

[0026] 2. The attributes or specifications of similar products in the same series are very similar, which reduces the accuracy of captured data and requires manual differentiation and matching, thus reducing efficiency

For example, the product names are "Epson Light Black Ink Cartridge T096720" and "Epson Matte Black Ink Cartridge T096820", and the product numbers are "T096720" and "T096820" respectively. The string similarity of the main attribute is very high. If there are only one product, it is necessary to manually select the best matching object, the search efficiency is low, and the real-time performance is poor

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