Fast intelligent commodity recommendation system

Abstract

The invention discloses a fast intelligent commodity recommendation system. The recommendation system provides personalized services for customers according to own demands of a commodity provider. The recommendation system comprises a merchant demand analysis module and a commodity recommendation module. A merchant demand is expressed in a form of rules; and a customer transaction database stores transaction records of a merchant. The merchant demand analysis module evaluates commodities operated by the merchant according to the merchant demand and the customer transaction database to generate a commodity library to be recommended. The commodity recommendation module generates a recommended commodity list meeting the own requirement of a current customer according to the customer transaction database and the commodity library to be recommended to provide commodity services for the current customer. The commodity recommendation generated by the system is not only customized for the customer, but also is customized for the merchant, can greatly increase the sales and the profits for the merchant, can improve the service speed of the commodity recommendation system to realize real-time service, has high expandability, and can meet the requirements of large merchants.

Description

technical field [0001] The invention relates to a commodity recommendation system, in particular to a fast and intelligent commodity recommendation system. Background technique [0002] Commodities include products and services. Product recommendations, including advertisements and discount programs, are the core means of merchant promotion. Traditional product recommendations are impersonal and cannot meet individual needs. Providing personalized product recommendation is a hot spot in global business at present, which can greatly improve the effect of product recommendation. For example, Amazon in the United States, after using the personalized product recommendation system in 2003, the annual sales volume increased by 25%. Large supermarkets or online sellers in the United States, such as Wal-Mart, Target, CVS, and Amazon, all attach great importance to personalized product recommendations. Personalized product recommendations are not yet common in China. The existing...

AI Technical Summary

Problems solved by technology

[0003] 1. Neglecting the needs of merchants, a truly intelligent product recommendation system must not only consider the personal needs of customers, but also the needs of merchants. For example, some best-selling products can be sold quickly even without advertising. out;

[0004] 2. It cannot handle massive data. Large merchants have tens of thousands of products and hundreds of thousands of transactions per day, accumulating massive transaction data. These transaction data are of great help in providing intelligent product recommendations, but how to deal with them Such a massive data flow is a very difficult problem. If it cannot be solved, the response time of the recommendation system will be very slow, the system will not be practical, or it can only be used for small businesses, but not for large department stores and supermarket chains;

[0005] 3. The response to market changes is slow, and many product recommendation systems are updated slowly. The model is updated only once a week or a month, and there is a lack of fast update methods

Images