Multidimensional information fusion-based comprehensive e-commerce product scoring method

Abstract

The invention discloses a multidimensional information fusion-based comprehensive e-commerce product scoring method. The method comprises the following steps of: obtaining multidimensional informationsuch as shop information, sales volume information and comment text information of e-commerce products; data preprocessing: carrying out data cleaning and data conversion on numerical type data, andcarrying out word segmentation and part-of-speech tagging on comment texts; mining the multidimensional information: carrying out data reduction and principal component regression analysis on the shopinformation and the commodity sales volume information to obtain shop information indexes and commodity sales volume indexes, carrying out emotion analysis on the comment texts, and obtaining a product feature score radar map through a quantification method and a clustering method; and commodity total score calculation: designing a fusion function and calculating a commodity total score. The method can be applied to commodity information-based commodity recommendation systems, is capable of efficiently and conveniently recognizing high-quality commodities so as to ensure that the designed recommendation systems are more rapid and correct.

Description

technical field [0001] The invention relates to the fields of natural language processing and data mining, in particular to a commodity evaluation method based on multi-dimensional information. Background technique [0002] With the continuous improvement of Internet information technology and the rapid development of the e-commerce industry, the e-commerce platform has become an important channel for online shopping. But at the same time, consumers often face some difficulties when shopping online, such as counterfeit and shoddy products, false publicity, and difficulty in purchasing. Although many e-commerce platforms provide consumer feedback mechanisms, facing the accumulation of feedback information on the Internet, how to quickly and effectively identify valuable reference information from the vast amount of feedback information, and how to pass high-value reference information , It is a crucial issue to evaluate the pros and cons of products concisely and efficiently...

AI Technical Summary

Problems solved by technology

[0003] However, the above-mentioned scholars’ analysis of commodities either only considers the structured data in the consumer feedback information, and calculates the value through the model of the structured data to measure the pros and cons of the commodity or the risk of commodity purchase.

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