Online hot commodity predicting method and system

Abstract

An online hot commodity predicting method includes that an online hot commodity logic control module M3 controls a data acquisition interface module M1 to acquire a commodity list required to be predicted according to demand, to read historical access and deal making data of all commodities in the list and to structuralize the historical access and the deal making data, controls an intelligent parameter setting module M4 to automatically set parameters required by hot commodity prediction according to the historical data, controls a predicting module M5 to predict hot selling indexes of all the commodities according to the historical data and the predicting parameters set by the intelligent parameter setting module M4, and controls a ranking output module M2 to rank all the commodities according to the hot selling indexes computed by the predicting module. The invention further discloses a system for implementing the online hot commodity predicting method.

Description

Technical field: [0001] The invention belongs to hot trend prediction technology in the field of network data mining, and specifically relates to a method for predicting hot-selling commodities in an Internet e-commerce system. Background technique [0002] With the development and popularization of the Internet, e-commerce is developing at an incredible speed, greatly changing people's life style. Statistics from iResearch show that the transaction scale of China's e-commerce market in 2013 was 9.9 trillion yuan, a year-on-year increase of 21.3%. It is estimated that the scale of e-commerce market in 2017 will reach 21.6 trillion yuan. E-commerce has changed the way people shop, and for many, online shopping has become an important part of everyday spending. Merchants in various retail industries have also set foot in the e-commerce market, and the sales model has shifted from traditional offline sales to online sales. [0003] The products on the e-commerce platform are ...

AI Technical Summary

Problems solved by technology

[0004] At present, there are mainly two types of methods for predicting online hot-selling products: one is based on the current trend of the industry (related news, public opinion guidance, mainstream values, seasonal characteristics, etc.), artificial analysis and forecasting based on comprehensive analysis of experience, this method The subjectivity is high, the labor cost is high, there is no commonality among various industries, and the relative deviation is relatively large; the other type is to conduct simple statistics on historical data such as product sales evaluation records on e-commerce platforms, and then predict hot-selling products. This method is versatile, but its application value is not high. It can only predict the products that have been sold well and will continue to sell well, but cannot predict the products that are not currently sold but will be sold well.

At present, there is a lack of a general-purpose prediction method for online hot-selling commodities, which can effectively mine the upcoming hot-selling commodities by making full use of historical sales evaluation data

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