基于BP算法的移动电子商务信息推送应用研究

发布时间：2018-01-16 21:03

本文关键词：基于BP算法的移动电子商务信息推送应用研究　出处：《北方民族大学》2017年硕士论文　论文类型：学位论文

【摘要】：由于互联网、移动设备等信息技术和服务行业的迅猛发展,目前的电子商务朝着一种新的方向发展,以移动性和个性化为主要特征的移动电子商务逐渐被大众所熟知。移动电子商务因为其方便快捷的交易方式,以及购买商品的便利性,得到了越来越多人的喜爱。尽管移动电子商务的快速发展给我们带来了很多方便,但是商品的种类和数量也日趋繁多,站点的网站结构也越发复杂,这使得用户很难在短期内寻找到自己想要的信息。移动电子商务推荐系统的发展为其行业带来了前所未有的机遇。一个成功的推荐系统就像是另一个自己,只不过它扮演着服务者的角色,它能够很清楚用户自己本身的喜好,非常清楚用户想要的,进而可以提高客户对网站的满意度和忠实度,进而提高业务绩效。本文在以上背景的基础上主要做了以下工作。首先是通过分析移动端电子商务特点和性质,对电子商务推荐系统中常用到的数据挖掘技术和推荐技术进行对比分析;其次基于数据挖掘算法的现有研究成果,针对数据挖掘方法的BP(误差反向传播算法)算法固有的缺陷进行改进,主要思路是通过改进GA(遗传算法)适应度函数后来对BP算法进行优化,并叙述了新模型的核心思想和算法流程;然后在此基础上对其进行实验测试和性能分析。此外,本文在分析移动电商推荐系统基本架构的基础上,结合当前推荐应用中出现的新需求,提出了一种跨平台的移动电子商务多推荐系统模型(Cross-platform Mobile E-commerce Multi-recommendation System,CP-MEMRS),并给出了模型的基本框架,并且对该模型在实际应用中可能遇到的困难进行了分析。
[Abstract]:With the rapid development of information technology and service industry, such as Internet, mobile devices, electronic commerce is developing in a new direction. Mobile E-commerce, which is characterized by mobility and personalization, is gradually known by the public. Mobile E-commerce is because of its convenient and quick way of trading, as well as the convenience of buying goods. Although the rapid development of mobile electronic commerce has brought us a lot of convenience, but the variety and quantity of goods is becoming more and more diverse, the site structure is becoming more and more complex. This makes it difficult for users to find the information they want in the short term. The development of mobile e-commerce recommendation system brings unprecedented opportunities for their industry. A successful recommendation system is like another one. Only it acts as a server, it can be very clear about the user's own preferences, very clear users want, and thus improve customer satisfaction and loyalty to the site. Then improve business performance. Based on the above background, this paper mainly does the following work. First of all, through the analysis of the characteristics and properties of mobile e-commerce. Compare and analyze the data mining technology and the recommendation technology which are often used in the E-commerce recommendation system. Secondly, based on the existing research results of the data mining algorithm, the inherent defects of the BP (error back Propagation algorithm) algorithm of the data mining method are improved. The main idea is to optimize BP algorithm by improving GA (genetic algorithm) fitness function, and describe the core idea and algorithm flow of the new model. In addition, based on the analysis of the basic architecture of mobile e-commerce recommendation system, combined with the new requirements of the current recommended applications. In this paper, a cross-platform multi-recommendation system model for mobile e-commerce is proposed. Cross-platform Mobile E-commerce Multi-recommendation System. The basic frame of the model is given, and the difficulties that may be encountered in the practical application of the model are analyzed.
【学位授予单位】：北方民族大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724.6;TP18;TP391.3

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