Personalized information recommending system and method

Abstract

The invention discloses a personalized information recommending system. The system comprises a geographical location information acquisition device, a transaction database server, an analysis server, a recommending server, a user client and a merchant client, wherein the input end of the analysis server is respectively connected with the geographical location information acquisition device and the transaction database server; the input end of the recommending server is connected with the output end of the analysis server; the user client is in two-way connection with the recommending server; the merchant client is in two-way connection with the recommending server. Through the system, clustering analysis can be performed on transaction data of a user based on the geographical location information technology, and situation model analysis can be performed according to the geographical location of the user, the clustering analysis result and the situation analysis result are combined so that more effective personalized recommending can be performed, and a basis is provided for accurate marketing of a merchant.

Description

technical field [0001] The invention relates to the field of information recommendation, in particular to a personalized information recommendation system and a recommendation method thereof. Background technique [0002] With the development of mobile communication networks and intelligent terminals, mobile e-commerce has ushered in unprecedented development. Personalized recommendation, as an effective means to solve the problem of "information overload", has played a great role in improving the user experience of e-commerce websites, increasing the purchase conversion rate and cross-selling, etc., but the traditional personalized recommendation system is based on user Recommendations based on user behavior history such as purchase behavior and collection behavior do not take into account the unique spatiotemporal characteristics of mobile e-commerce users, and mobile terminal devices have poor processing capabilities, limited input and output capabilities, and weak wirele...

AI Technical Summary

Problems solved by technology

Personalized recommendation, as an effective means to solve the problem of "information overload", has played a great role in improving the user experience of e-commerce websites, increasing the purchase conversion rate and cross-selling, etc., but the traditional personalized recommendation system is based on user Recommendations based on user behavior history such as purchase behavior and collection behavior do not take into account the unique spatiotemporal characteristics of mobile e-commerce users, and mobile terminal devices have poor processing capabilities, limited input and output capabilities, and weak wireless network bandwidth. It has higher requirements for the accuracy of the recommendation, therefore, the traditional personalized recommendation system is not suitable for the personalized recommendation field in the era of mobile e-commerce

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