Intelligent shopping guide system and method of electronic commerce website

Abstract

The invention discloses an intelligent shopping guide system and method of an electronic commerce website. The intelligent shopping guide system comprises an external data interface module, an online transaction processing module, an online analysis processing module and a complex event processing module, wherein the external data interface module is used for acquiring and preprocessing various kinds of required data in an external server / terminal in real time; the online transaction processing module is used for storing updated data acquired by the external data interface module in real time; the online analysis processing module is used for analyzing data in the online transaction processing module and updating a shopping guide policy; and the complex event processing module is used for implementing the updating of the shopping guide policy in real time and responding a shopping guide request in real time. The intelligent shopping guide system and method can be used for collecting and analyzing current data and other relevant data in real time and predicting current demands of a user by applying a data mining technology as well as recommending a proper product to the user on the basis of the prediction in a proper manner.

Description

technical field [0001] The present invention relates to an intelligent shopping guide system and method, in particular to a system and method for providing intelligent shopping guide service for users of e-commerce websites by using mass data mining technology. Background technique [0002] With the development of network communication technology and the popularity of e-commerce, more and more people are getting used to shopping online. In the virtual environment of e-commerce, merchants can provide more and more types and quantities of products on the Internet, which leads to the fact that although users have more and more choices, they are more and more interested in finding the products they are interested in. Disaster. Different online and offline environments determine that users are neither willing to spend too much time looking for products on the endless e-commerce website, nor can it be possible to check the quality of products like in a store environment. These t...

AI Technical Summary

Problems solved by technology

[0005] 1. It is impossible to analyze massive amounts of data in real time, and the shopping guide (recommendation) strategies of many systems are therefore updated slowly. The shopping guide strategy is only updated once a day or even a month, resulting in a lag in changes in market and customer information

Domestic medium and large-scale e-commerce websites usually have more than 10,000 products, with more than 10 million visits per day, and have accumulated a large amount of user behavior data. These data are of great help in providing intelligent product shopping guides. Analyzing these massive data streams in real time and quickly updating the strategy of the shopping guide system will lose a lot of valuable information

Especially for new users, in the initial short-term interaction, the user can already be understood to a certain extent, but if the information is not applied in real time, the appropriate shopping guide for the user will affect the user's experience and reduce the cost of the shopping guide. Effect

[0006] 2. It is impossible for users to choose their favorite shopping guide form

Therefore, the existing shopping guide recommendation method cannot allow users to choose their favorite shopping guide method, so that users may not use the shopping guide service, thereby reducing the user's interest in browsing commercial websites, and ultimately leading to the loss of users

[0007] 3. The types of user terminals supported are limited

Although these new terminals have the advantage of being convenient to carry, the screen area is usually much smaller than that of a PC, so the existing shopping guide system may not be used on these mobile terminals, resulting in the loss of some potential users

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