Design on PKI-Based Anonymous Mobile Agent Security in E-Commerce

The security of mobile agent directly decides its usage width in e-commerce. Especially, to protect users＇ private information is becoming more important now and future. So an anonymous mobile agent security mechanism with the secure authentication infrastructure based on PKI （public key infrastructure） is proposed in the paper. The multi-agent system is programmed by java language and every agent must register itself in CA （certificate authority） before working in the net and express his legit identity which is temptly produced and used only once. The CA ensures the legal of all agents＇ identity which take part in communicaiton or trade. And every user agent identity only is used once which makes other agents cannot decipher users＇ private information. The security mechanism of the multi-agent system implements anonymity, integrity, data confidentiality of mobile agent based on the MH（multiple hop） integrity protection regard to PKI limit.

Optimization and Innovation of the Operation Model of Mobile Social E-commerce under AI Empowerment:Taking the Female Community Platform“Little Red Book”as an Example

This article takes the female community platform“Little Red Book”as an example to explore the optimization and innovation of mobile community e-commerce operation mode under Artificial Intelligence(AI)empowerment.Firstly,the relevant concepts were defined,and then the unique attributes of mobile community e-commerce were analyzed.As a typical representative of mobile community e-commerce,Little Red Book introduces the background and characteristics of its platform,analyzes its mobile community operation mode,and focuses on exploring how to establish a mobile community e-commerce platform and effective operation mode under the empowerment of AI technology,to provide some reference and inspiration for the development and operation of Little Red Book and other e-commerce platform enterprises.

Customer Tiered Purchase Forecast by Mobile Edge Computing Based on Pareto/NBD and SVR

Mobile edge computing is trending nowadays for its computation efficiency and privacy.The rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs)in the internet.This paper predicts the overall sales volume of the enterprise through the classic ARIMA model,and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts,so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers,thus helping the enterprise to make layered and accurate marketing of new and old customers through machine learning.In general,machine learning relies on powerful computation and storage resources,while mobile edge computing typically provides limited computation resources locally.Therefore,it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

Socialization, Traffic Distribution and E-Commerce Trends:An Interpretation of the “Pinduoduo” Phenomenon

The article takes China’s e-commerce as the research object.Starting from the macro level of e-commerce development and taking the rapid rise of“Pinduoduo”as an example,it discusses the“traffic dilemma”and its influence in the traditional e-commerce platform.This discovers the internal mechanism of mobile e-commerce to solve the problem of traffic distribution mechanism by socialization.After that,this study compares the difference between traditional e-commerce and social-commerce systematically,and concludes that traditional e-commerce platform is a necessary process of the development of social-commerce.Socialization is an important trend of the development of traditional e-commerce and social-commerce will promote the realization of C2B model.

The Mobile Personalized Recommendation Model Containing Implicit Intention

Because mobile e-commerce is limited by the mobile terminal,network environment and other factors,accurate personalized recommendations become more and more important.We establish a large data intelligence platform,aiming at the characteristics of mobile e-commerce;we put forward a personalized recommendation model with implicit intention further.Firstly,create an intelligence unit with the virtual individual association set,virtual demand association set and virtual behavior associated set;Secondly,calculate the complex buying behavior prediction engine;Finally,give the predictive value of complex buying behavior.This method takes full account of factors such as hidden wishes perturbations that affect the predict of complex buying behavior,which to some extent solve a long-span composite purchasing behavior prediction.It shows that this method improves the purchasing behavior prediction accuracy effectively through experiments.

从E到M：我们欲向何方？

这一两年e-commerce的字样，随处。可见。电视、报纸、杂志各种媒体，甚至因应所需而产生的各种研讨会，都不难看见将“e”突出并且个性化。各个公司也推出不同的标语Slogan或服务。惠普公司首先喊出e-Service；趋势科技推出eDoctor服务性产品；IBM也有e-customer标语来强调e时代客户的重要性。