System for sharing, obtaining and processing personalized information in instant communication system

Abstract

The invention discloses a system for sharing, obtaining and processing personalized information in an instant communication system. The system comprises a user client side, a merchant client side, ananalysis module, a proportion module, a recommendation module, a geographic position module and a trading server, wherein the analysis module receives personalized information which is sent from the user client side and the merchant client side and is related to input behaviors and carries out clustering on a corresponding user to obtain at least one category; the proportion module obtains the shared resources of each category according to the personalized information sent from the client side corresponding to the user in each category; and the recommendation module pushes the geographic position obtained by the shared resources according to the geographic position module to the same category of user client sides.

Description

technical field [0001] The invention relates to the field of information recommendation, in particular to a personalized information sharing acquisition processing system in an instant messaging system. 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 ...

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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